Biok study: biomarker analysis
Stephen Murata
1/15/2022
1 Sample characteristics (raw variables)
compareGroups::descrTable(~.
, Biok_vert_df_raw,
hide.no = '0',
show.p.overall = FALSE,
include.label = TRUE)##
## --------Summary descriptives table ---------
##
## _____________________________________________________________
## [ALL] N
## N=218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 218
## male 82 (37.6%)
## female 136 (62.4%)
## age 44.3 (12.9) 218
## BMI 28.6 (5.58) 218
## race: 218
## asian 3 (1.38%)
## black 4 (1.83%)
## white 211 (96.8%)
## patientno 1016 (1365) 218
## Site_Location: 218
## Johns Hopkins 22 (10.1%)
## Univeristy of Michigan 69 (31.7%)
## Mayo Clinic 114 (52.3%)
## Pine Rest 13 (5.96%)
## infusionno: 218
## BL 73 (33.5%)
## 1st 72 (33.0%)
## 3rd 73 (33.5%)
## Blood_Draw_Event: 218
## Acute Infusion #1 Baseline 100 49 (22.5%)
## Acute Infusion #1 Baseline 40 24 (11.0%)
## Acute Infusion #1 Stop 100 48 (22.0%)
## Acute Infusion #1 Stop 40 24 (11.0%)
## Acute Infusion #3 Stop 100 28 (12.8%)
## Acute Infusion #3 Stop 40 45 (20.6%)
## Batch_Number 3.51 (1.71) 218
## BSS_Score 4.57 (6.82) 213
## MADRS_Score 17.0 (10.6) 217
## Remission: 215
## No remission 77 (35.8%)
## Remitter 138 (64.2%)
## TRP_nM 25449 (5942) 218
## five_HT_nM 205 (284) 218
## KYN_nM 917 (244) 218
## three_HK_nM 16.2 (7.44) 218
## KYNA_nM 19.5 (7.68) 218
## PIC_nM 19.2 (13.5) 218
## Quin_nM 146 (43.0) 218
## AA_nM 5.95 (3.12) 218
## KYN_TRP_ratio 0.04 (0.01) 218
## KYN_SER_ratio 33.2 (72.1) 218
## QUIN_PIC_ratio 9.14 (4.39) 218
## QUIN_KYNA_ratio 8.30 (3.40) 218
## threeHK_KYN_ratio 0.02 (0.01) 218
## threeHK_KYNA_ratio 0.91 (0.44) 218
## IL1B_pg_mL 0.28 (0.47) 42
## IL1B_pg_mL_LLOD 0.34 (0.19) 218
## IL2_pg_mL 1.27 (6.00) 152
## IL2_pg_mL_LLOD 0.92 (5.03) 218
## IL4_pg_mL 0.09 (0.03) 217
## IL4_pg_mL_LLOD 0.09 (0.03) 218
## IL6_pg_mL 1.04 (0.58) 218
## IL8_pg_mL 4.36 (1.66) 218
## IL10_pg_mL 0.64 (1.95) 218
## IL12p70_pg_mL 21.8 (186) 217
## IL12p70_pg_mL_LLOD 21.7 (185) 218
## IL13_pg_mL 5.07 (11.5) 215
## IL13_pg_mL_LLOD 5.01 (11.4) 218
## TNFa_pg_mL 1.44 (0.46) 218
## IFNy_pg_mL 6.97 (5.50) 218
## CRP_ng_mL 2835 (4774) 218
## NIC_nM 41.4 (340) 218
## NIC_nM_LLOD 41.4 (340) 218
## NTA_nM 306 (153) 218
## SAA_ng_mL 2870 (2145) 218
## VCAM_1_ng_mL 309 (67.9) 218
## ICAM_1_ng_mL 308 (82.3) 218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯2 Sample missingness counts
missingTable(compareGroups(infusionno~., data=Biok_vert_df_raw))##
## --------Missingness table by 'infusionno'---------
##
## _____________________________________________________________
## BL 1st 3rd p.overall
## N=73 N=72 N=73
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## age 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## BMI 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## race 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## patientno 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Site_Location 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Blood_Draw_Event 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Sample_ID 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Batch_Number 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## BSS_Score 0 (0.00%) 2 (2.78%) 3 (4.11%) 0.291
## MADRS_Score 0 (0.00%) 0 (0.00%) 1 (1.37%) 1.000
## Remission 1 (1.37%) 1 (1.39%) 1 (1.37%) 1.000
## TRP_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## five_HT_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## three_HK_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYNA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## PIC_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Quin_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## AA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_TRP_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_SER_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## QUIN_PIC_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## QUIN_KYNA_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## threeHK_KYN_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## threeHK_KYNA_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL1B_pg_mL 57 (78.1%) 61 (84.7%) 58 (79.5%) 0.565
## IL1B_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL2_pg_mL 28 (38.4%) 20 (27.8%) 18 (24.7%) 0.168
## IL2_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL4_pg_mL 0 (0.00%) 1 (1.39%) 0 (0.00%) 0.330
## IL4_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL6_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL8_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL10_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL12p70_pg_mL 0 (0.00%) 1 (1.39%) 0 (0.00%) 0.330
## IL12p70_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL13_pg_mL 1 (1.37%) 2 (2.78%) 0 (0.00%) 0.327
## IL13_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## TNFa_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IFNy_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## CRP_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NIC_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NIC_nM_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NTA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## SAA_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## VCAM_1_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## ICAM_1_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯3 Outcome inspection - Depressive severity (MADRS_Score)
mu <- ddply(Biok_vert_df, "infusionno", summarise, grp.mean=mean(MADRS_Score))
ggplot(Biok_vert_df, aes(x=MADRS_Score))+
geom_histogram(color="black", fill="orange")+
facet_grid(infusionno ~ .)+
theme(legend.position="none")+
geom_vline(data=mu, aes(xintercept=grp.mean, color=infusionno),linetype="dashed")+
labs(title="Distribution of MADRS total score by Ketamine infusion (timepoint)", x="Depressive severity (MADRS)", y="Count")+
theme_gray()
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = as.numeric(infusionno), y = MADRS_Score)) +
# geom_boxplot(aes(group = infusionno), lwd=1.25, fatten=1, outlier.shape = "triangle", outlier.size = 3 ) +
ggtitle("Depression by Ketamine Infusion (timeseries)")+
geom_line(aes(group=patientno, color=Remission)) +
geom_point(aes(color=Remission))+
labs(x = "infusionno") +
scale_x_continuous(breaks = 1:3)
my_comparisons <- list( c("BL", "1st"), c("1st", "3rd"), c("BL", "3rd") )
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = infusionno, y = MADRS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", ref.group="BL", comparisons=my_comparisons)+
ggpubr::stat_compare_means(method="anova", label.y=70)
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = Remission, y = MADRS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~infusionno)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=50)
4 Outcome inspection - suicidality (BSS_Score)
mu <- ddply(Biok_vert_df, "infusionno", summarise,grp.mean=mean(log(BSS_Score)))
ggplot(Biok_vert_df, aes(x=log(BSS_Score)))+
geom_histogram(color="black", fill="orange")+
facet_grid(infusionno ~ .)+
theme(legend.position="none")+
geom_vline(data=mu, aes(xintercept=grp.mean, color=infusionno),linetype="dashed")+
labs(title="Distribution of BSS total score by Ketamine infusion (timepoint)", x="Suicidal severity (BSS)", y="Count")+
theme_gray()
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = as.numeric(infusionno), y = BSS_Score)) +
# geom_boxplot(aes(group = infusionno), lwd=1.25, fatten=1, outlier.shape = "triangle", outlier.size = 3 ) +
ggtitle("Suicidality by Ketamine Infusion (timeseries)")+
geom_line(aes(group=patientno, color=Remission)) +
geom_point(aes(color=Remission))+
labs(x = "infusionno") +
scale_x_continuous(breaks = 1:3)
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = infusionno, y = BSS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", ref.group="BL", comparisons=my_comparisons)+
ggpubr::stat_compare_means( label.y=40)
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = Remission, y = BSS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~infusionno)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means( label.y=30)
5 Univariate screen (whole group) by continuous outcome (Pearson’s matrix)
Bx_spearmans<- Biok_vert_df %>%
select("age", "BMI","MADRS_Score", "BSS_Score",
all_of(Biok_biomarkers)) %>%
select(!starts_with("IL1"))%>%
select(!starts_with("IL2")) %>%
select(!starts_with("IL4")) %>%
select(!contains("LLOD"))
mydata.cor = cor(Bx_spearmans, method = c("pearson"), use="complete.obs")
matrix<-Hmisc::rcorr(as.matrix(mydata.cor))
corrplot::corrplot(mydata.cor,
addCoef.col = 'black',
p.mat=matrix$P,
insig="blank",
order="hclust",
method="color",
type="upper",
diag=FALSE,
number.cex=0.8,
na.label.col = "gray",
addgrid.col=TRUE,
tl.col = 'red',
tl.srt = 45,
title="Pearson's matrix of age, BMI, and log transformed biomarkers (whole sample)")
6 Model 1 (mixed effects): TRP by Remission*infusionno
rlmer_TRP<-robustlmm::rlmer(TRP_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_TRP_vif<-lme4::lmer(TRP_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_TRP_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070739 1 1.034765
## age 1.117809 1 1.057265
## race 1.157261 2 1.037189
## BMI 1.175612 1 1.084256
## Remission 1.021103 1 1.010496
## infusionno 1.002135 2 1.000533car::qqPlot(residuals(rlmer_TRP), main="QQ-PLOT")
## 218 42
## 215 41pairwise_remission<-emmeans(rlmer_TRP, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1192 1332 Inf 0.895 0.3707
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 233 1344 Inf 0.174 0.8621
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1515 1334 Inf 1.135 0.2562
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer_TRP, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 3911 984 Inf 3.974 0.0002
## BL - 3rd 3104 971 Inf 3.197 0.0040
## 1st - 3rd -807 975 Inf -0.828 0.6856
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2952 727 Inf 4.059 0.0001
## BL - 3rd 3427 727 Inf 4.711 <.0001
## 1st - 3rd 474 723 Inf 0.656 0.7888
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates7 MODEL 1: five_HT_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(five_HT_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(five_HT_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072494 1 1.035613
## age 1.121970 1 1.059231
## race 1.155203 2 1.036727
## BMI 1.176992 1 1.084893
## Remission 1.020156 1 1.010028
## infusionno 1.001426 2 1.000356car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 8 171
## 7 168pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 126.8 48.8 Inf 2.599 0.0094
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 76.2 48.8 Inf 1.560 0.1188
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 134.7 48.8 Inf 2.760 0.0058
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 58.33 13.38 Inf 4.361 <.0001
## BL - 3rd 18.66 13.18 Inf 1.416 0.3327
## 1st - 3rd -39.67 13.19 Inf -3.008 0.0074
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 7.78 9.85 Inf 0.790 0.7095
## BL - 3rd 26.60 9.85 Inf 2.701 0.0189
## 1st - 3rd 18.82 9.77 Inf 1.927 0.1311
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates8 MODEL 1: KYN_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071703 1 1.035231
## age 1.119733 1 1.058174
## race 1.158377 2 1.037439
## BMI 1.177280 1 1.085025
## Remission 1.020022 1 1.009961
## infusionno 1.001483 2 1.000370car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 218 42
## 215 41pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 57.0 50.7 Inf 1.124 0.2611
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 35.1 51.0 Inf 0.689 0.4911
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 52.9 50.8 Inf 1.042 0.2974
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 98.72 27.1 Inf 3.645 0.0008
## BL - 3rd 86.33 26.7 Inf 3.234 0.0035
## 1st - 3rd -12.39 26.7 Inf -0.463 0.8885
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 76.81 20.0 Inf 3.847 0.0004
## BL - 3rd 82.25 20.0 Inf 4.119 0.0001
## 1st - 3rd 5.43 19.8 Inf 0.274 0.9594
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates9 MODEL 1: three_HK_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(three_HK_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(three_HK_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073076 1 1.035894
## age 1.123067 1 1.059749
## race 1.156273 2 1.036967
## BMI 1.177900 1 1.085311
## Remission 1.019673 1 1.009788
## infusionno 1.001016 2 1.000254car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 55
## 41 54pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.353 1.16 Inf 0.303 0.7619
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.359 1.17 Inf 0.307 0.7589
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.459 1.17 Inf 0.394 0.6936
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.8854 0.598 Inf 1.480 0.3004
## BL - 3rd 0.8995 0.590 Inf 1.526 0.2788
## 1st - 3rd 0.0142 0.591 Inf 0.024 0.9997
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.8917 0.441 Inf 2.022 0.1070
## BL - 3rd 1.0062 0.441 Inf 2.281 0.0584
## 1st - 3rd 0.1144 0.438 Inf 0.261 0.9630
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates10 MODEL 1: KYNA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(KYNA_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYNA_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071293 1 1.035033
## age 1.119630 1 1.058126
## race 1.153283 2 1.036296
## BMI 1.175083 1 1.084012
## Remission 1.021432 1 1.010659
## infusionno 1.002190 2 1.000547car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 218
## 41 215pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.288 1.63 Inf -0.176 0.8600
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.787 1.65 Inf -0.479 0.6323
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.595 1.64 Inf -0.363 0.7165
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.349 1.013 Inf 1.332 0.3775
## BL - 3rd 1.578 0.999 Inf 1.579 0.2546
## 1st - 3rd 0.228 1.002 Inf 0.228 0.9718
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.850 0.748 Inf 1.137 0.4915
## BL - 3rd 1.271 0.748 Inf 1.700 0.2053
## 1st - 3rd 0.421 0.743 Inf 0.567 0.8376
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates11 MODEL 1: PIC_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(PIC_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(PIC_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070013 1 1.034414
## age 1.116520 1 1.056655
## race 1.154117 2 1.036484
## BMI 1.173581 1 1.083319
## Remission 1.022859 1 1.011365
## infusionno 1.002743 2 1.000685car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 159
## 41 156pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.1322 1.81 Inf 0.073 0.9417
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0174 1.82 Inf -0.010 0.9924
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2489 1.81 Inf -0.137 0.8907
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.410 1.025 Inf 2.351 0.0491
## BL - 3rd 1.733 1.010 Inf 1.716 0.1993
## 1st - 3rd -0.676 1.013 Inf -0.668 0.7822
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.260 0.756 Inf 2.989 0.0079
## BL - 3rd 1.352 0.756 Inf 1.788 0.1735
## 1st - 3rd -0.908 0.751 Inf -1.209 0.4475
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates12 MODEL 1: Quin_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(Quin_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(Quin_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072170 1 1.035457
## age 1.120654 1 1.058610
## race 1.158733 2 1.037519
## BMI 1.177951 1 1.085335
## Remission 1.019672 1 1.009788
## infusionno 1.001187 2 1.000297car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 61 218
## 60 215pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.944 10.3 Inf -0.188 0.8505
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.875 10.3 Inf -0.085 0.9326
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -4.091 10.3 Inf -0.396 0.6919
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.581 4.52 Inf 0.350 0.9348
## BL - 3rd 0.976 4.46 Inf 0.219 0.9739
## 1st - 3rd -0.605 4.46 Inf -0.136 0.9899
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.650 3.33 Inf 0.795 0.7059
## BL - 3rd -1.171 3.33 Inf -0.351 0.9342
## 1st - 3rd -3.821 3.31 Inf -1.156 0.4799
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates13 MODEL 1: AA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(AA_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(AA_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071595 1 1.035179
## age 1.120229 1 1.058409
## race 1.153724 2 1.036395
## BMI 1.175568 1 1.084236
## Remission 1.021072 1 1.010481
## infusionno 1.002010 2 1.000502car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 50 44
## 49 43pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.189 0.523 Inf -2.274 0.0230
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.489 0.528 Inf -0.926 0.3546
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.170 0.524 Inf -0.324 0.7459
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.373 0.413 Inf -0.904 0.6375
## BL - 3rd -0.161 0.407 Inf -0.396 0.9170
## 1st - 3rd 0.212 0.409 Inf 0.518 0.8627
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.327 0.305 Inf 1.071 0.5321
## BL - 3rd 0.858 0.305 Inf 2.811 0.0137
## 1st - 3rd 0.531 0.303 Inf 1.750 0.1867
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates14 MODEL 1: KYN_TRP_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_TRP_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_TRP_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072837 1 1.035778
## age 1.122619 1 1.059537
## race 1.155824 2 1.036867
## BMI 1.177528 1 1.085140
## Remission 1.019862 1 1.009882
## infusionno 1.001187 2 1.000297car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 162 55
## 159 54pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.001265 0.00205 Inf 0.616 0.5381
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.001760 0.00207 Inf 0.852 0.3944
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.000769 0.00206 Inf 0.374 0.7087
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001523 0.001108 Inf -1.374 0.3544
## BL - 3rd -0.001046 0.001093 Inf -0.958 0.6036
## 1st - 3rd 0.000477 0.001095 Inf 0.435 0.9008
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001029 0.000817 Inf -1.258 0.4189
## BL - 3rd -0.001543 0.000817 Inf -1.887 0.1424
## 1st - 3rd -0.000514 0.000812 Inf -0.633 0.8017
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates15 MODEL 1: KYN_SER_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_SER_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_SER_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.068114 1 1.033496
## age 1.113700 1 1.055320
## race 1.158779 2 1.037529
## BMI 1.171466 1 1.082343
## Remission 1.023286 1 1.011576
## infusionno 1.003092 2 1.000772car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 177 28
## 174 27pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -4.60 4.26 Inf -1.081 0.2797
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -5.74 4.26 Inf -1.346 0.1784
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -8.04 4.26 Inf -1.889 0.0588
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.148 1.53 Inf -0.751 0.7331
## BL - 3rd -0.735 1.51 Inf -0.488 0.8772
## 1st - 3rd 0.413 1.51 Inf 0.274 0.9594
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -2.286 1.13 Inf -2.030 0.1052
## BL - 3rd -4.177 1.13 Inf -3.709 0.0006
## 1st - 3rd -1.892 1.12 Inf -1.693 0.2078
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates16 MODEL 1: QUIN_PIC_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(QUIN_PIC_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(QUIN_PIC_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071772 1 1.035264
## age 1.119889 1 1.058248
## race 1.159842 2 1.037767
## BMI 1.177882 1 1.085303
## Remission 1.019529 1 1.009717
## infusionno 1.001210 2 1.000302car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 152 134
## 149 132pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.348 0.943 Inf -0.369 0.7122
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.246 0.947 Inf -0.260 0.7950
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.295 0.944 Inf -0.313 0.7545
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.896 0.437 Inf -2.053 0.0998
## BL - 3rd -0.700 0.430 Inf -1.627 0.2342
## 1st - 3rd 0.196 0.431 Inf 0.455 0.8920
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.794 0.322 Inf -2.469 0.0362
## BL - 3rd -0.647 0.322 Inf -2.012 0.1094
## 1st - 3rd 0.147 0.319 Inf 0.460 0.8898
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates17 MODEL 1: QUIN_KYNA_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(QUIN_KYNA_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(QUIN_KYNA_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070881 1 1.034834
## age 1.118450 1 1.057568
## race 1.159853 2 1.037769
## BMI 1.176719 1 1.084767
## Remission 1.019871 1 1.009887
## infusionno 1.001615 2 1.000404car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 152 134
## 149 132pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.263 0.668 Inf 0.393 0.6941
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.198 0.671 Inf 0.295 0.7677
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.146 0.668 Inf 0.219 0.8268
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.206 0.354 Inf -0.582 0.8298
## BL - 3rd -0.385 0.349 Inf -1.103 0.5122
## 1st - 3rd -0.179 0.349 Inf -0.512 0.8657
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.270 0.261 Inf -1.036 0.5539
## BL - 3rd -0.501 0.261 Inf -1.920 0.1329
## 1st - 3rd -0.231 0.259 Inf -0.891 0.6463
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates18 MODEL 1: threeHK_KYN_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(threeHK_KYN_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(threeHK_KYN_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073326 1 1.036015
## age 1.123404 1 1.059908
## race 1.157300 2 1.037198
## BMI 1.178502 1 1.085588
## Remission 1.019407 1 1.009657
## infusionno 1.000766 2 1.000191car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 217 42
## 214 41pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000829 0.00105 Inf -0.789 0.4304
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000364 0.00106 Inf -0.344 0.7309
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000624 0.00105 Inf -0.592 0.5538
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -8.42e-04 0.000543 Inf -1.550 0.2676
## BL - 3rd -3.03e-04 0.000535 Inf -0.567 0.8379
## 1st - 3rd 5.38e-04 0.000536 Inf 1.004 0.5743
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -3.76e-04 0.000400 Inf -0.939 0.6159
## BL - 3rd -9.75e-05 0.000400 Inf -0.244 0.9678
## 1st - 3rd 2.78e-04 0.000397 Inf 0.700 0.7635
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"19 MODEL 1: threeHK_KYNA_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(threeHK_KYNA_ratio~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(threeHK_KYNA_ratio~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071771 1 1.035264
## age 1.120132 1 1.058363
## race 1.161233 2 1.038078
## BMI 1.178396 1 1.085540
## Remission 1.018957 1 1.009434
## infusionno 1.000895 2 1.000224car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 4 152
## 3 149pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0272 0.0776 Inf 0.351 0.7257
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0396 0.0779 Inf 0.508 0.6112
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0127 0.0777 Inf 0.163 0.8705
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01074 0.0349 Inf 0.308 0.9491
## BL - 3rd 0.02395 0.0344 Inf 0.696 0.7655
## 1st - 3rd 0.01321 0.0344 Inf 0.384 0.9221
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.02313 0.0257 Inf 0.899 0.6407
## BL - 3rd 0.00939 0.0257 Inf 0.365 0.9292
## 1st - 3rd -0.01374 0.0255 Inf -0.538 0.8523
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"20 MODEL 1: IL1B_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL1B_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL1B_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.244521 1 1.115581
## age 1.261477 1 1.123155
## race 1.272183 1 1.127911
## BMI 1.243487 1 1.115118
## Remission 1.133553 1 1.064684
## infusionno 1.081089 2 1.019683car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 18 90
## 1 18pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0769 0.0789 Inf 0.975 0.3297
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0905 0.0900 Inf -1.006 0.3146
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0264 0.0776 Inf 0.340 0.7340
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.09292 0.0682 Inf 1.363 0.3606
## BL - 3rd 0.00893 0.0575 Inf 0.155 0.9868
## 1st - 3rd -0.08398 0.0678 Inf -1.239 0.4302
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.07449 0.0429 Inf -1.738 0.1911
## BL - 3rd -0.04164 0.0408 Inf -1.021 0.5633
## 1st - 3rd 0.03285 0.0414 Inf 0.794 0.7067
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"21 MODEL 1: IL1B_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL1B_pg_mL_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL1B_pg_mL_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.067724 1 1.033308
## age 1.110784 1 1.053937
## race 1.154230 2 1.036509
## BMI 1.168989 1 1.081198
## Remission 1.028915 1 1.014355
## infusionno 1.003362 2 1.000839car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 196 18
## 193 17pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1.73e-14 6.17e-15 Inf 2.812 0.0049
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 2.22e-14 6.24e-15 Inf 3.550 0.0004
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.03e-14 6.16e-15 Inf -1.675 0.0939
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -5.14e-15 7.00e-15 Inf -0.735 0.7425
## BL - 3rd -4.03e-15 6.93e-15 Inf -0.581 0.8301
## 1st - 3rd 1.12e-15 7.00e-15 Inf 0.159 0.9861
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -3.23e-16 5.21e-15 Inf -0.062 0.9979
## BL - 3rd -3.17e-14 5.21e-15 Inf -6.082 <.0001
## 1st - 3rd -3.14e-14 5.20e-15 Inf -6.029 <.0001
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"22 MODEL 1: IL2_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL2_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL2_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.074108 1 1.036392
## age 1.142629 1 1.068938
## race 1.246542 2 1.056639
## BMI 1.246339 1 1.116395
## Remission 1.034167 1 1.016940
## infusionno 1.000076 2 1.000019car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 80 8
## 49 4pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.029922 0.0695 Inf 0.431 0.6668
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000285 0.0670 Inf -0.004 0.9966
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.059588 0.0662 Inf 0.900 0.3683
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0834 0.0509 Inf 1.639 0.2292
## BL - 3rd -0.0162 0.0517 Inf -0.313 0.9475
## 1st - 3rd -0.0995 0.0495 Inf -2.013 0.1090
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0532 0.0376 Inf 1.416 0.3326
## BL - 3rd 0.0135 0.0373 Inf 0.361 0.9305
## 1st - 3rd -0.0397 0.0345 Inf -1.148 0.4842
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"23 MODEL 1: IL2_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL2_pg_mL_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL2_pg_mL_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072296 1 1.035517
## age 1.121523 1 1.059020
## race 1.163538 2 1.038592
## BMI 1.179893 1 1.086229
## Remission 1.017890 1 1.008905
## infusionno 1.000014 2 1.000003car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 80 8
## 78 7pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.00311 0.0605 Inf -0.051 0.9590
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.01427 0.0607 Inf -0.235 0.8140
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00481 0.0605 Inf 0.079 0.9367
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01809 0.0252 Inf 0.716 0.7538
## BL - 3rd -0.02917 0.0249 Inf -1.172 0.4697
## 1st - 3rd -0.04726 0.0249 Inf -1.897 0.1395
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.00693 0.0186 Inf 0.372 0.9264
## BL - 3rd -0.02126 0.0186 Inf -1.143 0.4877
## 1st - 3rd -0.02819 0.0185 Inf -1.527 0.2783
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"24 MODEL 1: IL4_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL4_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL4_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071388 1 1.035079
## age 1.120097 1 1.058347
## race 1.160698 2 1.037958
## BMI 1.177810 1 1.085269
## Remission 1.019149 1 1.009529
## infusionno 1.001244 2 1.000311car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 45 120
## 44 117pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00270 0.00651 Inf 0.416 0.6777
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00405 0.00655 Inf 0.619 0.5357
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00123 0.00651 Inf 0.189 0.8504
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001963 0.00347 Inf -0.565 0.8387
## BL - 3rd -0.000155 0.00343 Inf -0.045 0.9989
## 1st - 3rd 0.001809 0.00343 Inf 0.527 0.8581
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.000614 0.00258 Inf -0.238 0.9693
## BL - 3rd -0.001631 0.00256 Inf -0.637 0.7999
## 1st - 3rd -0.001017 0.00256 Inf -0.397 0.9169
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"25 MODEL 1: IL4_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL4_pg_mL_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL4_pg_mL_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071099 1 1.034939
## age 1.119039 1 1.057846
## race 1.160638 2 1.037945
## BMI 1.177326 1 1.085046
## Remission 1.019358 1 1.009633
## infusionno 1.001316 2 1.000329car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 101 120
## 99 118pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00272 0.00655 Inf 0.415 0.6779
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00474 0.00658 Inf 0.721 0.4708
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00134 0.00655 Inf 0.204 0.8384
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.97e-03 0.00352 Inf -0.560 0.8414
## BL - 3rd -1.91e-04 0.00347 Inf -0.055 0.9983
## 1st - 3rd 1.78e-03 0.00348 Inf 0.512 0.8655
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 5.18e-05 0.00260 Inf 0.020 0.9998
## BL - 3rd -1.57e-03 0.00260 Inf -0.605 0.8171
## 1st - 3rd -1.63e-03 0.00258 Inf -0.630 0.8036
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"26 MODEL 1: IL6_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL6_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL6_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072532 1 1.035631
## age 1.122043 1 1.059265
## race 1.155271 2 1.036743
## BMI 1.177052 1 1.084920
## Remission 1.020121 1 1.010011
## infusionno 1.001400 2 1.000350car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 81 111
## 79 109pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0119 0.0842 Inf -0.141 0.8878
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.1293 0.0847 Inf 1.526 0.1269
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0414 0.0844 Inf 0.491 0.6236
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.13980 0.0448 Inf -3.120 0.0051
## BL - 3rd 0.01162 0.0442 Inf 0.263 0.9626
## 1st - 3rd 0.15142 0.0443 Inf 3.421 0.0018
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.00135 0.0330 Inf 0.041 0.9991
## BL - 3rd 0.06490 0.0330 Inf 1.964 0.1212
## 1st - 3rd 0.06355 0.0328 Inf 1.937 0.1282
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates27 MODEL 1: IL8_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL8_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL8_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.069932 1 1.034375
## age 1.116458 1 1.056626
## race 1.158489 2 1.037464
## BMI 1.174739 1 1.083854
## Remission 1.021320 1 1.010604
## infusionno 1.002362 2 1.000590car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 22 45
## 21 44pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.384 0.321 Inf -1.196 0.2317
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.378 0.324 Inf -1.168 0.2427
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.259 0.322 Inf 0.805 0.4211
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1368 0.225 Inf 0.607 0.8163
## BL - 3rd 0.1861 0.222 Inf 0.837 0.6799
## 1st - 3rd 0.0493 0.223 Inf 0.221 0.9734
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1430 0.167 Inf 0.859 0.6661
## BL - 3rd 0.8291 0.167 Inf 4.980 <.0001
## 1st - 3rd 0.6861 0.165 Inf 4.147 0.0001
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates28 MODEL 1: IL10_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL10_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL10_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.074279 1 1.036475
## age 1.125280 1 1.060792
## race 1.158715 2 1.037515
## BMI 1.179753 1 1.086164
## Remission 1.018896 1 1.009404
## infusionno 1.000097 2 1.000024car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 46 173
## 45 170pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0031 0.0301 Inf 0.103 0.9180
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0122 0.0302 Inf 0.403 0.6868
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0212 0.0301 Inf 0.705 0.4809
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.010627 0.01195 Inf -0.890 0.6469
## BL - 3rd -0.019358 0.01177 Inf -1.644 0.2271
## 1st - 3rd -0.008730 0.01179 Inf -0.741 0.7393
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001546 0.00880 Inf -0.176 0.9831
## BL - 3rd -0.001211 0.00880 Inf -0.138 0.9896
## 1st - 3rd 0.000335 0.00873 Inf 0.038 0.9992
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates29 MODEL 1: IL12p70_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL12p70_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL12p70_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073451 1 1.036075
## age 1.123322 1 1.059869
## race 1.159640 2 1.037721
## BMI 1.179549 1 1.086071
## Remission 1.018963 1 1.009437
## infusionno 1.000412 2 1.000103car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 134 63pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0283 0.0356 Inf 0.795 0.4267
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0607 0.0358 Inf 1.694 0.0902
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0369 0.0356 Inf 1.036 0.3004
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.01753 0.0197 Inf -0.890 0.6465
## BL - 3rd -0.00495 0.0194 Inf -0.255 0.9648
## 1st - 3rd 0.01257 0.0195 Inf 0.646 0.7944
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01488 0.0146 Inf 1.017 0.5663
## BL - 3rd 0.00367 0.0145 Inf 0.253 0.9654
## 1st - 3rd -0.01121 0.0145 Inf -0.771 0.7206
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"30 MODEL 1: IL12p70_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL12p70_pg_mL_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL12p70_pg_mL_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073885 1 1.036284
## age 1.124563 1 1.060454
## race 1.157885 2 1.037329
## BMI 1.179150 1 1.085887
## Remission 1.019121 1 1.009515
## infusionno 1.000407 2 1.000102car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 135 63pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0280 0.0358 Inf 0.782 0.4340
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0640 0.0360 Inf 1.777 0.0755
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0369 0.0359 Inf 1.027 0.3042
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.01755 0.0199 Inf -0.881 0.6523
## BL - 3rd -0.00507 0.0196 Inf -0.258 0.9640
## 1st - 3rd 0.01249 0.0197 Inf 0.634 0.8012
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01845 0.0147 Inf 1.255 0.4207
## BL - 3rd 0.00377 0.0147 Inf 0.256 0.9644
## 1st - 3rd -0.01468 0.0146 Inf -1.006 0.5730
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates31 MODEL 1: IL13_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL13_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL13_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073946 1 1.036314
## age 1.124178 1 1.060272
## race 1.160375 2 1.037886
## BMI 1.180224 1 1.086381
## Remission 1.018731 1 1.009322
## infusionno 1.000030 2 1.000007car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 133 62pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2468 0.178 Inf -1.385 0.1660
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0983 0.179 Inf -0.548 0.5836
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.1442 0.178 Inf -0.810 0.4182
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0934 0.0959 Inf -0.974 0.5935
## BL - 3rd -0.0616 0.0945 Inf -0.652 0.7914
## 1st - 3rd 0.0318 0.0947 Inf 0.335 0.9399
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0551 0.0725 Inf 0.760 0.7276
## BL - 3rd 0.0409 0.0713 Inf 0.574 0.8341
## 1st - 3rd -0.0142 0.0714 Inf -0.198 0.9785
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates32 MODEL 1: IL13_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL13_pg_mL_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL13_pg_mL_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.073941 1 1.036311
## age 1.124168 1 1.060268
## race 1.160324 2 1.037874
## BMI 1.180195 1 1.086368
## Remission 1.018741 1 1.009327
## infusionno 1.000034 2 1.000009car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 101
## 135 99pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2315 0.181 Inf -1.279 0.2008
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0614 0.182 Inf -0.338 0.7357
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.1459 0.181 Inf -0.805 0.4208
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0939 0.1007 Inf -0.932 0.6200
## BL - 3rd -0.0614 0.0993 Inf -0.619 0.8098
## 1st - 3rd 0.0324 0.0995 Inf 0.326 0.9431
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0762 0.0743 Inf 1.026 0.5602
## BL - 3rd 0.0242 0.0743 Inf 0.326 0.9432
## 1st - 3rd -0.0520 0.0737 Inf -0.706 0.7601
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates33 MODEL 1: TNFa_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(TNFa_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(TNFa_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.074025 1 1.036352
## age 1.124818 1 1.060574
## race 1.158177 2 1.037394
## BMI 1.179364 1 1.085985
## Remission 1.019038 1 1.009474
## infusionno 1.000298 2 1.000075car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 101 212
## 99 209pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.00455 0.0797 Inf -0.057 0.9545
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.01155 0.0799 Inf -0.145 0.8850
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00044 0.0798 Inf 0.006 0.9956
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.04374 0.0278 Inf 1.574 0.2570
## BL - 3rd 0.03070 0.0274 Inf 1.121 0.5010
## 1st - 3rd -0.01304 0.0274 Inf -0.476 0.8828
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.03674 0.0205 Inf 1.795 0.1714
## BL - 3rd 0.03569 0.0205 Inf 1.743 0.1891
## 1st - 3rd -0.00105 0.0203 Inf -0.052 0.9985
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates34 MODEL 1: IFNy_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IFNy_pg_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IFNy_pg_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.069806 1 1.034314
## age 1.115896 1 1.056360
## race 1.157322 2 1.037203
## BMI 1.174108 1 1.083563
## Remission 1.022165 1 1.011022
## infusionno 1.002614 2 1.000653car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 167 173
## 164 170pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.090 0.659 Inf 0.137 0.8913
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.069 0.661 Inf -0.105 0.9168
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.318 0.659 Inf -0.483 0.6293
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.368 0.240 Inf 1.537 0.2735
## BL - 3rd 0.206 0.236 Inf 0.873 0.6576
## 1st - 3rd -0.162 0.236 Inf -0.687 0.7713
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.209 0.176 Inf 1.186 0.4617
## BL - 3rd -0.202 0.176 Inf -1.146 0.4859
## 1st - 3rd -0.412 0.175 Inf -2.350 0.0492
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates35 MODEL 1: CRP_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(CRP_ng_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(CRP_ng_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072183 1 1.035463
## age 1.120640 1 1.058603
## race 1.159969 2 1.037795
## BMI 1.178414 1 1.085548
## Remission 1.019332 1 1.009620
## infusionno 1.000984 2 1.000246car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 210 155
## 207 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 768 455 Inf 1.689 0.0912
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 732 455 Inf 1.609 0.1076
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 618 455 Inf 1.358 0.1744
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 120.3 108.3 Inf 1.111 0.5075
## BL - 3rd 284.3 106.7 Inf 2.664 0.0211
## 1st - 3rd 164.0 106.8 Inf 1.536 0.2740
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 84.6 79.7 Inf 1.061 0.5387
## BL - 3rd 133.9 79.7 Inf 1.680 0.2130
## 1st - 3rd 49.4 79.1 Inf 0.624 0.8069
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"36 MODEL 1: NIC_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(NIC_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NIC_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.074166 1 1.036420
## age 1.125074 1 1.060695
## race 1.158474 2 1.037460
## BMI 1.179580 1 1.086085
## Remission 1.018958 1 1.009434
## infusionno 1.000188 2 1.000047car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 10 155
## 9 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0831 0.149 Inf -0.556 0.5784
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2776 0.150 Inf -1.846 0.0649
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0221 0.150 Inf 0.148 0.8825
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1013 0.0861 Inf 1.176 0.4673
## BL - 3rd -0.0298 0.0849 Inf -0.351 0.9345
## 1st - 3rd -0.1311 0.0851 Inf -1.540 0.2721
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0932 0.0635 Inf -1.467 0.3067
## BL - 3rd 0.0754 0.0635 Inf 1.187 0.4611
## 1st - 3rd 0.1687 0.0631 Inf 2.673 0.0205
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates37 MODEL 1: NIC_nM_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(NIC_nM_LLOD~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NIC_nM_LLOD~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.074166 1 1.036420
## age 1.125074 1 1.060695
## race 1.158474 2 1.037460
## BMI 1.179580 1 1.086085
## Remission 1.018958 1 1.009434
## infusionno 1.000188 2 1.000047car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 10 155
## 9 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0850 0.149 Inf -0.571 0.5683
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2773 0.150 Inf -1.849 0.0644
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0223 0.149 Inf 0.149 0.8815
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1013 0.0857 Inf 1.181 0.4644
## BL - 3rd -0.0298 0.0845 Inf -0.352 0.9338
## 1st - 3rd -0.1311 0.0847 Inf -1.547 0.2691
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0910 0.0633 Inf -1.438 0.3211
## BL - 3rd 0.0775 0.0633 Inf 1.225 0.4381
## 1st - 3rd 0.1685 0.0628 Inf 2.683 0.0200
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates38 MODEL 1: NTA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(NTA_nM~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NTA_nM~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070668 1 1.034731
## age 1.118355 1 1.057523
## race 1.152475 2 1.036115
## BMI 1.174062 1 1.083542
## Remission 1.022283 1 1.011080
## infusionno 1.002534 2 1.000633car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 22 32
## 21 31pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -35.118 34.5 Inf -1.018 0.3085
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -66.694 34.8 Inf -1.917 0.0552
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.616 34.5 Inf -0.018 0.9858
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 43.0 25.4 Inf 1.692 0.2081
## BL - 3rd 14.8 25.1 Inf 0.591 0.8249
## 1st - 3rd -28.2 25.2 Inf -1.120 0.5016
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 11.5 18.8 Inf 0.610 0.8149
## BL - 3rd 49.3 18.8 Inf 2.625 0.0236
## 1st - 3rd 37.9 18.7 Inf 2.028 0.1056
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates39 MODEL 1: SAA_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(SAA_ng_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(SAA_ng_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070877 1 1.034832
## age 1.118748 1 1.057709
## race 1.160592 2 1.037934
## BMI 1.177020 1 1.084905
## Remission 1.019419 1 1.009663
## infusionno 1.001409 2 1.000352car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 148 163
## 145 160pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 530 412 Inf 1.285 0.1987
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 494 413 Inf 1.197 0.2311
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 480 412 Inf 1.164 0.2446
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 100.1010 132.5 Inf 0.755 0.7305
## BL - 3rd 114.7529 130.6 Inf 0.879 0.6538
## 1st - 3rd 14.6519 130.7 Inf 0.112 0.9931
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 64.7704 97.6 Inf 0.664 0.7846
## BL - 3rd 64.7580 97.6 Inf 0.663 0.7847
## 1st - 3rd -0.0123 96.8 Inf 0.000 1.0000
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates40 MODEL 1: VCAM_1_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(VCAM_1_ng_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(VCAM_1_ng_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.072482 1 1.035607
## age 1.121484 1 1.059002
## race 1.157416 2 1.037224
## BMI 1.177796 1 1.085263
## Remission 1.019786 1 1.009845
## infusionno 1.001191 2 1.000298car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 53 212
## 52 209pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 6.69 15.7 Inf 0.425 0.6709
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 16.49 15.8 Inf 1.044 0.2967
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 20.19 15.8 Inf 1.281 0.2001
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.684 7.27 Inf -0.094 0.9951
## BL - 3rd 2.871 7.16 Inf 0.401 0.9153
## 1st - 3rd 3.555 7.17 Inf 0.495 0.8735
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 9.119 5.36 Inf 1.702 0.2043
## BL - 3rd 16.373 5.36 Inf 3.056 0.0063
## 1st - 3rd 7.254 5.32 Inf 1.364 0.3597
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates41 MODEL 1: ICAM_1_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(ICAM_1_ng_mL~sex+age+race+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(ICAM_1_ng_mL~sex+age+race+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.071482 1 1.035124
## age 1.119649 1 1.058135
## race 1.160944 2 1.038013
## BMI 1.177931 1 1.085326
## Remission 1.019134 1 1.009522
## infusionno 1.001085 2 1.000271car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 17 15
## 16 14pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 24.7 17.3 Inf 1.432 0.1523
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 23.3 17.3 Inf 1.346 0.1784
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 30.8 17.3 Inf 1.781 0.0749
##
## Results are averaged over the levels of: sex, racepairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 4.48 7.32 Inf 0.613 0.8131
## BL - 3rd 7.47 7.21 Inf 1.036 0.5543
## 1st - 3rd 2.98 7.22 Inf 0.413 0.9101
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 3.07 5.39 Inf 0.570 0.8363
## BL - 3rd 13.53 5.39 Inf 2.509 0.0324
## 1st - 3rd 10.46 5.35 Inf 1.955 0.1236
##
## Results are averaged over the levels of: sex, race
## P value adjustment: tukey method for comparing a family of 3 estimates42 MODEL 2: MADRS by TRP*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+TRP_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by TRP*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 0.95 | 0.64 – 1.26 | <0.001 | <0.001 |
| age | 0.02 | -0.06 – 0.11 | 0.598 | 0.598 |
| sex [female] | 0.25 | 0.07 – 0.43 | 0.005 | 0.005 |
| BMI | 0.03 | -0.06 – 0.11 | 0.534 | 0.534 |
| TRP nM | 0.02 | -0.13 – 0.17 | 0.813 | 0.813 |
| infusionno [1st] | -1.26 | -1.47 – -1.06 | <0.001 | <0.001 |
| infusionno [3rd] | -1.81 | -2.02 – -1.61 | <0.001 | <0.001 |
| TRP nM * infusionno [1st] | 0.10 | -0.10 – 0.30 | 0.331 | 0.331 |
| TRP nM * infusionno [3rd] | 0.14 | -0.07 – 0.34 | 0.192 | 0.192 |
| Random Effects | ||||
| σ2 | 39.00 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.15 | |||
| ICC | 0.14 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.610 / 0.663 | |||
43 MODEL 2: MADRS by five_HT_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+five_HT_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## [1] 85 13sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by five_HT_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.05 | 0.71 – 1.39 | <0.001 | <0.001 |
| age | 0.03 | -0.06 – 0.11 | 0.494 | 0.494 |
| sex [female] | 0.16 | -0.01 – 0.33 | 0.066 | 0.066 |
| BMI | 0.04 | -0.05 – 0.12 | 0.394 | 0.394 |
| five HT nM | -0.03 | -0.16 – 0.10 | 0.641 | 0.641 |
| infusionno [1st] | -1.29 | -1.48 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.83 | -2.03 – -1.64 | <0.001 | <0.001 |
|
five HT nM * infusionno [1st] |
0.08 | -0.12 – 0.27 | 0.432 | 0.432 |
|
five HT nM * infusionno [3rd] |
0.29 | 0.09 – 0.48 | 0.004 | 0.004 |
| Random Effects | ||||
| σ2 | 38.75 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.84 | |||
| ICC | 0.19 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.597 / 0.672 | |||
44 MODEL 2: MADRS by KYN_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+KYN_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by KYN_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 0.99 | 0.67 – 1.31 | <0.001 | <0.001 |
| age | -0.01 | -0.10 – 0.08 | 0.858 | 0.858 |
| sex [female] | 0.22 | 0.04 – 0.39 | 0.014 | 0.014 |
| BMI | 0.02 | -0.07 – 0.10 | 0.728 | 0.728 |
| KYN nM | -0.02 | -0.16 – 0.12 | 0.780 | 0.780 |
| infusionno [1st] | -1.28 | -1.48 – -1.07 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.04 – -1.64 | <0.001 | <0.001 |
| KYN nM * infusionno [1st] | 0.11 | -0.10 – 0.31 | 0.298 | 0.298 |
| KYN nM * infusionno [3rd] | 0.14 | -0.06 – 0.34 | 0.166 | 0.166 |
| Random Effects | ||||
| σ2 | 39.71 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.99 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.599 / 0.659 | |||
45 MODEL 2: MADRS by three_HK_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+three_HK_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 104
## 147 104sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by three_HK_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.69 – 1.36 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.11 | 0.805 | 0.805 |
| sex [female] | 0.18 | -0.01 – 0.37 | 0.067 | 0.067 |
| BMI | 0.02 | -0.07 – 0.12 | 0.671 | 0.671 |
| three HK nM | -0.03 | -0.14 – 0.09 | 0.643 | 0.643 |
| infusionno [1st] | -1.28 | -1.47 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.02 – -1.65 | <0.001 | <0.001 |
|
three HK nM * infusionno [1st] |
0.05 | -0.14 – 0.23 | 0.620 | 0.620 |
|
three HK nM * infusionno [3rd] |
0.09 | -0.11 – 0.29 | 0.357 | 0.357 |
| Random Effects | ||||
| σ2 | 35.01 | |||
| τ00 patientno | 4.69 | |||
| τ00 Site_Location | 7.88 | |||
| ICC | 0.26 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.590 / 0.698 | |||
46 MODEL 2: MADRS by KYNA_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+KYNA_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 104
## 147 104sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by KYNA_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.36 | <0.001 | <0.001 |
| age | 0.01 | -0.09 – 0.11 | 0.813 | 0.813 |
| sex [female] | 0.17 | -0.03 – 0.37 | 0.093 | 0.093 |
| BMI | 0.02 | -0.07 – 0.12 | 0.651 | 0.651 |
| KYNA nM | -0.06 | -0.18 – 0.07 | 0.386 | 0.386 |
| infusionno [1st] | -1.29 | -1.47 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.03 – -1.65 | <0.001 | <0.001 |
|
KYNA nM * infusionno [1st] |
0.09 | -0.11 – 0.28 | 0.404 | 0.404 |
|
KYNA nM * infusionno [3rd] |
0.06 | -0.12 – 0.24 | 0.531 | 0.531 |
| Random Effects | ||||
| σ2 | 34.60 | |||
| τ00 patientno | 5.26 | |||
| τ00 Site_Location | 7.56 | |||
| ICC | 0.27 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.590 / 0.701 | |||
47 MODEL 2: MADRS by PIC_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+PIC_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 85 148
## 85 147sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by PIC_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.00 | 0.68 – 1.32 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.865 | 0.865 |
| sex [female] | 0.21 | 0.04 – 0.38 | 0.017 | 0.017 |
| BMI | 0.03 | -0.05 – 0.12 | 0.449 | 0.449 |
| PIC nM | -0.01 | -0.11 – 0.09 | 0.833 | 0.833 |
| infusionno [1st] | -1.25 | -1.45 – -1.05 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
| PIC nM * infusionno [1st] | 0.31 | 0.04 – 0.58 | 0.025 | 0.025 |
| PIC nM * infusionno [3rd] | 0.12 | -0.10 – 0.34 | 0.297 | 0.297 |
| Random Effects | ||||
| σ2 | 39.90 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.48 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.599 / 0.662 | |||
48 MODEL 2: MADRS by Quin_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+Quin_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by Quin_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.37 | <0.001 | <0.001 |
| age | 0.02 | -0.07 – 0.11 | 0.714 | 0.714 |
| sex [female] | 0.16 | -0.02 – 0.34 | 0.080 | 0.080 |
| BMI | 0.03 | -0.05 – 0.12 | 0.458 | 0.458 |
| Quin nM | 0.00 | -0.15 – 0.15 | 0.978 | 0.978 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | 0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
Quin nM * infusionno [1st] |
-0.01 | -0.22 – 0.19 | 0.890 | 0.890 |
|
Quin nM * infusionno [3rd] |
-0.07 | -0.27 – 0.13 | 0.485 | 0.485 |
| Random Effects | ||||
| σ2 | 41.12 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.84 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.587 / 0.653 | |||
49 MODEL 2: MADRS by AA_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+AA_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by AA_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.71 – 1.34 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.751 | 0.751 |
| sex [female] | 0.17 | -0.00 – 0.35 | 0.051 | 0.051 |
| BMI | 0.03 | -0.05 – 0.12 | 0.450 | 0.450 |
| AA nM | -0.07 | -0.19 – 0.05 | 0.233 | 0.233 |
| infusionno [1st] | -1.31 | -1.51 – -1.11 | <0.001 | <0.001 |
| infusionno [3rd] | -1.87 | -2.07 – -1.66 | <0.001 | <0.001 |
| AA nM * infusionno [1st] | 0.13 | -0.06 – 0.33 | 0.174 | 0.174 |
| AA nM * infusionno [3rd] | 0.02 | -0.21 – 0.25 | 0.846 | 0.846 |
| Random Effects | ||||
| σ2 | 41.13 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.11 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.653 | |||
50 MODEL 2: MADRS by AA_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+AA_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by AA_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.71 – 1.34 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.751 | 0.751 |
| sex [female] | 0.17 | -0.00 – 0.35 | 0.051 | 0.051 |
| BMI | 0.03 | -0.05 – 0.12 | 0.450 | 0.450 |
| AA nM | -0.07 | -0.19 – 0.05 | 0.233 | 0.233 |
| infusionno [1st] | -1.31 | -1.51 – -1.11 | <0.001 | <0.001 |
| infusionno [3rd] | -1.87 | -2.07 – -1.66 | <0.001 | <0.001 |
| AA nM * infusionno [1st] | 0.13 | -0.06 – 0.33 | 0.174 | 0.174 |
| AA nM * infusionno [3rd] | 0.02 | -0.21 – 0.25 | 0.846 | 0.846 |
| Random Effects | ||||
| σ2 | 41.13 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.11 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.653 | |||
51 MODEL 2: MADRS by KYN_TRP_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+KYN_TRP_ratio*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by KYN_TRP_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.33 | <0.001 | <0.001 |
| age | 0.02 | -0.07 – 0.11 | 0.675 | 0.675 |
| sex [female] | 0.18 | 0.01 – 0.35 | 0.042 | 0.042 |
| BMI | 0.03 | -0.06 – 0.12 | 0.473 | 0.473 |
| KYN TRP ratio | -0.07 | -0.23 – 0.09 | 0.382 | 0.382 |
| infusionno [1st] | -1.28 | -1.49 – -1.08 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.64 | <0.001 | <0.001 |
|
KYN TRP ratio * infusionno [1st] |
0.06 | -0.15 – 0.26 | 0.592 | 0.592 |
|
KYN TRP ratio * infusionno [3rd] |
0.09 | -0.12 – 0.29 | 0.404 | 0.404 |
| Random Effects | ||||
| σ2 | 40.99 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.39 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.590 / 0.653 | |||
52 MODEL 2: MADRS by KYN_SER_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+KYN_SER_ratio*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 104
## 147 104sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by KYN_SER_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.69 – 1.37 | <0.001 | <0.001 |
| age | 0.00 | -0.09 – 0.10 | 0.938 | 0.938 |
| sex [female] | 0.17 | -0.02 – 0.35 | 0.075 | 0.075 |
| BMI | 0.02 | -0.07 – 0.12 | 0.621 | 0.621 |
| KYN SER ratio | 0.10 | -0.03 – 0.24 | 0.141 | 0.141 |
| infusionno [1st] | -1.28 | -1.46 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.83 | -2.01 – -1.64 | <0.001 | <0.001 |
|
KYN SER ratio * infusionno [1st] |
-0.19 | -0.42 – 0.04 | 0.104 | 0.104 |
|
KYN SER ratio * infusionno [3rd] |
-0.18 | -0.36 – -0.01 | 0.040 | 0.040 |
| Random Effects | ||||
| σ2 | 33.81 | |||
| τ00 patientno | 4.54 | |||
| τ00 Site_Location | 8.73 | |||
| ICC | 0.28 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.594 / 0.708 | |||
53 MODEL 2: MADRS by QUIN_PIC_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+TRP_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by QUIN_PIC_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 0.95 | 0.64 – 1.26 | <0.001 | <0.001 |
| age | 0.02 | -0.06 – 0.11 | 0.598 | 0.598 |
| sex [female] | 0.25 | 0.07 – 0.43 | 0.005 | 0.005 |
| BMI | 0.03 | -0.06 – 0.11 | 0.534 | 0.534 |
| TRP nM | 0.02 | -0.13 – 0.17 | 0.813 | 0.813 |
| infusionno [1st] | -1.26 | -1.47 – -1.06 | <0.001 | <0.001 |
| infusionno [3rd] | -1.81 | -2.02 – -1.61 | <0.001 | <0.001 |
| TRP nM * infusionno [1st] | 0.10 | -0.10 – 0.30 | 0.331 | 0.331 |
| TRP nM * infusionno [3rd] | 0.14 | -0.07 – 0.34 | 0.192 | 0.192 |
| Random Effects | ||||
| σ2 | 39.00 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.15 | |||
| ICC | 0.14 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.610 / 0.663 | |||
54 MODEL 2: MADRS by QUIN_KYNA_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+QUIN_KYNA_ratio*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by QUIN_KYNA_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.69 – 1.35 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.859 | 0.859 |
| sex [female] | 0.19 | 0.03 – 0.36 | 0.023 | 0.023 |
| BMI | 0.03 | -0.05 – 0.11 | 0.499 | 0.499 |
| QUIN KYNA ratio | 0.05 | -0.08 – 0.19 | 0.453 | 0.453 |
| infusionno [1st] | -1.30 | -1.49 – -1.10 | 0.002 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.66 | <0.001 | <0.001 |
|
QUIN KYNA ratio * infusionno [1st] |
-0.18 | -0.39 – 0.02 | 0.077 | 0.077 |
|
QUIN KYNA ratio * infusionno [3rd] |
-0.11 | -0.30 – 0.08 | 0.237 | 0.237 |
| Random Effects | ||||
| σ2 | 38.67 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.00 | |||
| ICC | 0.17 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.602 / 0.671 | |||
55 MODEL 2: MADRS by threeHK_KYN_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+threeHK_KYN_ratio*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by threeHK_KYN_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.33 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.906 | 0.906 |
| sex [female] | 0.18 | 0.01 – 0.36 | 0.042 | 0.042 |
| BMI | 0.03 | -0.06 – 0.12 | 0.511 | 0.511 |
| threeHK KYN ratio | -0.02 | -0.17 – 0.12 | 0.750 | 0.750 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.64 | <0.001 | <0.001 |
|
threeHK KYN ratio * infusionno [1st] |
-0.00 | -0.21 – 0.20 | 0.967 | 0.967 |
|
threeHK KYN ratio * infusionno [3rd] |
-0.02 | -0.22 – 0.19 | 0.874 | 0.874 |
| Random Effects | ||||
| σ2 | 41.64 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.21 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.587 / 0.648 | |||
56 MODEL 2: MADRS by threeHK_KYNA_ratio*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+threeHK_KYNA_ratio*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by threeHK_KYNA_ratio*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.34 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.846 | 0.846 |
| sex [female] | 0.18 | 0.00 – 0.36 | 0.044 | 0.044 |
| BMI | 0.02 | -0.06 – 0.11 | 0.601 | 0.601 |
| threeHK KYNA ratio | 0.00 | -0.13 – 0.13 | 0.975 | 0.975 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
threeHK KYNA ratio * infusionno [1st] |
-0.08 | -0.28 – 0.12 | 0.434 | 0.434 |
|
threeHK KYNA ratio * infusionno [3rd] |
0.05 | -0.14 – 0.25 | 0.586 | 0.586 |
| Random Effects | ||||
| σ2 | 40.57 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.38 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.655 | |||
57 MODEL 2: MADRS by IL1B_pg_mL*infusionno
rlmer<-robustlmm::rlmer(IL1B_pg_mL~age+sex+BMI+TRP_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 18 90
## 1 18sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL1B_pg_mL*infusionno")| IL 1 B pg m L | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | -0.24 | -0.70 – 0.23 | 0.770 | 0.317 |
| age | 0.03 | -0.09 – 0.15 | 0.631 | 0.631 |
| sex [female] | 0.21 | -0.04 – 0.46 | 0.095 | 0.095 |
| BMI | -0.05 | -0.16 – 0.06 | 0.379 | 0.379 |
| TRP nM | 0.09 | -0.04 – 0.22 | 0.179 | 0.179 |
| infusionno [1st] | 0.13 | -0.02 – 0.29 | 0.903 | 0.086 |
| infusionno [3rd] | 0.12 | -0.02 – 0.26 | 0.252 | 0.100 |
| TRP nM * infusionno [1st] | 0.02 | -0.11 – 0.16 | 0.748 | 0.748 |
| TRP nM * infusionno [3rd] | -0.07 | -0.23 – 0.09 | 0.381 | 0.381 |
| Random Effects | ||||
| σ2 | 0.01 | |||
| τ00 patientno | 0.01 | |||
| τ00 Site_Location | 0.03 | |||
| ICC | 0.90 | |||
| N patientno | 26 | |||
| N Site_Location | 4 | |||
| Observations | 42 | |||
| Marginal R2 / Conditional R2 | 0.056 / 0.905 | |||
58 MODEL 2: MADRS by IL1B_pg_mL_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL1B_pg_mL_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL1B_pg_mL_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.69 – 1.35 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.852 | 0.852 |
| sex [female] | 0.18 | 0.01 – 0.35 | 0.042 | 0.042 |
| BMI | 0.02 | -0.06 – 0.11 | 0.573 | 0.573 |
| IL1B pg mL LLOD | -0.01 | -0.18 – 0.17 | 0.945 | 0.945 |
| infusionno [1st] | -1.30 | -1.51 – -1.10 | 0.114 | <0.001 |
| infusionno [3rd] | -1.84 | -2.05 – -1.64 | <0.001 | <0.001 |
|
IL1B pg mL LLOD * infusionno [1st] |
-0.23 | -0.83 – 0.38 | 0.465 | 0.465 |
|
IL1B pg mL LLOD * infusionno [3rd] |
-0.03 | -0.24 – 0.17 | 0.741 | 0.741 |
| Random Effects | ||||
| σ2 | 41.14 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.57 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.588 / 0.652 | |||
59 MODEL 2: MADRS by IL2_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL2_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 98 54sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL2_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.14 | 0.81 – 1.47 | <0.001 | <0.001 |
| age | -0.02 | -0.12 – 0.09 | 0.756 | 0.756 |
| sex [female] | 0.03 | -0.18 – 0.25 | 0.768 | 0.768 |
| BMI | 0.07 | -0.03 – 0.17 | 0.187 | 0.187 |
| IL2 pg mL | 0.05 | -0.12 – 0.22 | 0.535 | 0.535 |
| infusionno [1st] | -1.29 | -1.53 – -1.04 | <0.001 | <0.001 |
| infusionno [3rd] | -1.93 | -2.17 – -1.69 | <0.001 | <0.001 |
|
IL2 pg mL * infusionno [1st] |
0.02 | -0.22 – 0.26 | 0.889 | 0.889 |
|
IL2 pg mL * infusionno [3rd] |
0.09 | -0.15 – 0.32 | 0.463 | 0.463 |
| Random Effects | ||||
| σ2 | 38.85 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 5.16 | |||
| ICC | 0.12 | |||
| N patientno | 60 | |||
| N Site_Location | 4 | |||
| Observations | 152 | |||
| Marginal R2 / Conditional R2 | 0.615 / 0.660 | |||
60 MODEL 2: MADRS by IL2_pg_mL_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL2_pg_mL_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL2_pg_mL_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.71 – 1.32 | <0.001 | <0.001 |
| age | 0.02 | -0.07 – 0.11 | 0.660 | 0.660 |
| sex [female] | 0.16 | -0.01 – 0.33 | 0.067 | 0.067 |
| BMI | 0.03 | -0.05 – 0.12 | 0.481 | 0.481 |
| IL2 pg mL LLOD | 0.04 | -0.10 – 0.18 | 0.537 | 0.537 |
| infusionno [1st] | -1.29 | -1.49 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.86 | -2.06 – -1.66 | <0.001 | <0.001 |
|
IL2 pg mL LLOD * infusionno [1st] |
0.02 | -0.18 – 0.22 | 0.867 | 0.867 |
|
IL2 pg mL LLOD * infusionno [3rd] |
0.06 | -0.13 – 0.26 | 0.526 | 0.526 |
| Random Effects | ||||
| σ2 | 40.19 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.42 | |||
| ICC | 0.14 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.602 / 0.657 | |||
61 MODEL 2: MADRS by IL4_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL4_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 146 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL4_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.04 | 0.69 – 1.38 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.900 | 0.900 |
| sex [female] | 0.16 | -0.01 – 0.33 | 0.072 | 0.072 |
| BMI | 0.02 | -0.06 – 0.11 | 0.603 | 0.603 |
| IL4 pg mL | -0.04 | -0.18 – 0.11 | 0.636 | 0.636 |
| infusionno [1st] | -1.28 | -1.48 – -1.08 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.05 – -1.64 | <0.001 | <0.001 |
|
IL4 pg mL * infusionno [1st] |
0.08 | -0.11 – 0.27 | 0.414 | 0.414 |
|
IL4 pg mL * infusionno [3rd] |
0.10 | -0.11 – 0.32 | 0.355 | 0.355 |
| Random Effects | ||||
| σ2 | 41.05 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.56 | |||
| ICC | 0.17 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 216 | |||
| Marginal R2 / Conditional R2 | 0.583 / 0.655 | |||
62 MODEL 2: MADRS by IL4_pg_mL_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL4_pg_mL_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL4_pg_mL_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.36 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.10 | 0.786 | 0.786 |
| sex [female] | 0.17 | -0.01 – 0.34 | 0.059 | 0.059 |
| BMI | 0.02 | -0.07 – 0.11 | 0.642 | 0.642 |
| IL4 pg mL LLOD | -0.04 | -0.19 – 0.11 | 0.642 | 0.642 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL4 pg mL LLOD * infusionno [1st] |
0.10 | -0.09 – 0.29 | 0.311 | 0.311 |
|
IL4 pg mL LLOD * infusionno [3rd] |
0.10 | -0.12 – 0.32 | 0.361 | 0.361 |
| Random Effects | ||||
| σ2 | 41.17 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.04 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.586 / 0.654 | |||
63 MODEL 2: MADRS by IL6_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL6_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL6_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.71 – 1.36 | <0.001 | <0.001 |
| age | 0.00 | -0.09 – 0.09 | 0.994 | 0.994 |
| sex [female] | 0.16 | -0.02 – 0.33 | 0.076 | 0.076 |
| BMI | 0.01 | -0.09 – 0.10 | 0.906 | 0.906 |
| IL6 pg mL | 0.04 | -0.12 – 0.19 | 0.631 | 0.631 |
| infusionno [1st] | -1.30 | -1.50 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.04 – -1.64 | <0.001 | <0.001 |
|
IL6 pg mL * infusionno [1st] |
0.06 | -0.14 – 0.25 | 0.556 | 0.556 |
|
IL6 pg mL * infusionno [3rd] |
0.04 | -0.19 – 0.27 | 0.720 | 0.720 |
| Random Effects | ||||
| σ2 | 40.32 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.79 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.659 | |||
64 MODEL 2: MADRS by IL8_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL8_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 85 148
## 85 147sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL8_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.70 – 1.34 | <0.001 | <0.001 |
| age | -0.00 | -0.09 – 0.08 | 0.927 | 0.927 |
| sex [female] | 0.19 | 0.02 – 0.36 | 0.031 | 0.031 |
| BMI | 0.03 | -0.05 – 0.12 | 0.457 | 0.457 |
| IL8 pg mL | -0.04 | -0.16 – 0.08 | 0.495 | 0.495 |
| infusionno [1st] | -1.31 | -1.50 – -1.11 | <0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.04 – -1.64 | <0.001 | <0.001 |
|
IL8 pg mL * infusionno [1st] |
0.21 | 0.01 – 0.40 | 0.039 | 0.039 |
|
IL8 pg mL * infusionno [3rd] |
0.14 | -0.07 – 0.34 | 0.186 | 0.186 |
| Random Effects | ||||
| σ2 | 39.25 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.37 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.603 / 0.666 | |||
65 MODEL 2: MADRS by IL10_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL10_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL10_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.33 | <0.001 | <0.001 |
| age | 0.02 | -0.07 – 0.10 | 0.725 | 0.725 |
| sex [female] | 0.19 | 0.02 – 0.36 | 0.032 | 0.032 |
| BMI | 0.03 | -0.06 – 0.11 | 0.563 | 0.563 |
| IL10 pg mL | -0.00 | -0.13 – 0.13 | 0.993 | 0.993 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL10 pg mL * infusionno [1st] |
-0.06 | -0.26 – 0.13 | 0.531 | 0.531 |
|
IL10 pg mL * infusionno [3rd] |
-0.09 | -0.29 – 0.12 | 0.413 | 0.413 |
| Random Effects | ||||
| σ2 | 40.99 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.08 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.592 / 0.652 | |||
66 MODEL 2: MADRS by IL12p70_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL12p70_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 146 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL12p70_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.04 | 0.70 – 1.38 | <0.001 | <0.001 |
| age | -0.00 | -0.09 – 0.09 | 0.941 | 0.941 |
| sex [female] | 0.15 | -0.02 – 0.33 | 0.089 | 0.089 |
| BMI | 0.03 | -0.05 – 0.12 | 0.454 | 0.454 |
| IL12p70 pg mL | -0.06 | -0.18 – 0.06 | 0.348 | 0.348 |
| infusionno [1st] | -1.28 | -1.48 – -1.08 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL12p70 pg mL * infusionno [1st] |
0.05 | -0.20 – 0.29 | 0.697 | 0.697 |
|
IL12p70 pg mL * infusionno [3rd] |
0.05 | -0.13 – 0.23 | 0.563 | 0.563 |
| Random Effects | ||||
| σ2 | 41.03 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.24 | |||
| ICC | 0.17 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 216 | |||
| Marginal R2 / Conditional R2 | 0.585 / 0.655 | |||
67 MODEL 2: MADRS by IL12p70_pg_mL_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL12p70_pg_mL_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL12p70_pg_mL_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.35 | <0.001 | <0.001 |
| age | 0.00 | -0.08 – 0.09 | 0.918 | 0.918 |
| sex [female] | 0.17 | -0.01 – 0.34 | 0.064 | 0.064 |
| BMI | 0.03 | -0.06 – 0.12 | 0.486 | 0.486 |
| IL12p70 pg mL LLOD | -0.06 | -0.18 – 0.07 | 0.365 | 0.365 |
| infusionno [1st] | -1.29 | -1.50 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL12p70 pg mL LLOD * infusionno [1st] |
0.05 | -0.19 – 0.30 | 0.676 | 0.676 |
|
IL12p70 pg mL LLOD * infusionno [3rd] |
0.05 | -0.13 – 0.23 | 0.562 | 0.562 |
| Random Effects | ||||
| σ2 | 41.29 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.44 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.589 / 0.651 | |||
68 MODEL 2: MADRS by IL13_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL13_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 144 84sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL13_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.37 | <0.001 | <0.001 |
| age | 0.00 | -0.09 – 0.09 | 0.945 | 0.945 |
| sex [female] | 0.16 | -0.02 – 0.34 | 0.075 | 0.075 |
| BMI | 0.03 | -0.06 – 0.12 | 0.522 | 0.522 |
| IL13 pg mL | -0.07 | -0.21 – 0.07 | 0.336 | 0.336 |
| infusionno [1st] | -1.26 | -1.46 – -1.06 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL13 pg mL * infusionno [1st] |
0.06 | -0.14 – 0.27 | 0.552 | 0.552 |
|
IL13 pg mL * infusionno [3rd] |
0.06 | -0.13 – 0.26 | 0.540 | 0.540 |
| Random Effects | ||||
| σ2 | 40.64 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.30 | |||
| ICC | 0.17 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 214 | |||
| Marginal R2 / Conditional R2 | 0.587 / 0.657 | |||
69 MODEL 2: MADRS by IL13_pg_mL_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IL13_pg_mL_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IL13_pg_mL_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.70 – 1.35 | <0.001 | <0.001 |
| age | 0.00 | -0.08 – 0.09 | 0.921 | 0.921 |
| sex [female] | 0.17 | -0.01 – 0.34 | 0.062 | 0.062 |
| BMI | 0.03 | -0.06 – 0.12 | 0.489 | 0.489 |
| IL13 pg mL LLOD | -0.07 | -0.21 – 0.07 | 0.350 | 0.350 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IL13 pg mL LLOD * infusionno [1st] |
0.07 | -0.13 – 0.28 | 0.490 | 0.490 |
|
IL13 pg mL LLOD * infusionno [3rd] |
0.06 | -0.14 – 0.26 | 0.545 | 0.545 |
| Random Effects | ||||
| σ2 | 41.29 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.41 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.589 / 0.651 | |||
70 MODEL 2: MADRS by TNFa_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+TNFa_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by TNFa_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.04 | 0.72 – 1.36 | <0.001 | <0.001 |
| age | 0.02 | -0.06 – 0.11 | 0.613 | 0.613 |
| sex [female] | 0.14 | -0.03 – 0.32 | 0.105 | 0.105 |
| BMI | 0.02 | -0.06 – 0.11 | 0.568 | 0.568 |
| TNFa pg mL | -0.12 | -0.26 – 0.03 | 0.130 | 0.130 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.86 | -2.06 – -1.66 | <0.001 | <0.001 |
|
TNFa pg mL * infusionno [1st] |
0.04 | -0.16 – 0.25 | 0.690 | 0.690 |
|
TNFa pg mL * infusionno [3rd] |
0.11 | -0.09 – 0.31 | 0.299 | 0.299 |
| Random Effects | ||||
| σ2 | 40.21 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.22 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.596 / 0.658 | |||
71 MODEL 2: MADRS by IFNy_pg_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+IFNy_pg_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by IFNy_pg_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.32 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.09 | 0.871 | 0.871 |
| sex [female] | 0.19 | 0.02 – 0.36 | 0.031 | 0.031 |
| BMI | 0.03 | -0.06 – 0.11 | 0.511 | 0.511 |
| IFNy pg mL | 0.07 | -0.05 – 0.20 | 0.249 | 0.249 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
IFNy pg mL * infusionno [1st] |
-0.14 | -0.33 – 0.05 | 0.143 | 0.143 |
|
IFNy pg mL * infusionno [3rd] |
-0.03 | -0.24 – 0.18 | 0.808 | 0.808 |
| Random Effects | ||||
| σ2 | 39.54 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.15 | |||
| ICC | 0.15 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.601 / 0.662 | |||
72 MODEL 2: MADRS by CRP_ng_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+CRP_ng_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 85 148
## 85 147sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by CRP_ng_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.71 – 1.34 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.778 | 0.778 |
| sex [female] | 0.17 | -0.01 – 0.34 | 0.062 | 0.062 |
| BMI | 0.01 | -0.08 – 0.10 | 0.842 | 0.842 |
| CRP ng mL | -0.02 | -0.16 – 0.12 | 0.778 | 0.778 |
| infusionno [1st] | -1.29 | -1.50 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.64 | <0.001 | <0.001 |
|
CRP ng mL * infusionno [1st] |
0.09 | -0.10 – 0.28 | 0.367 | 0.367 |
|
CRP ng mL * infusionno [3rd] |
0.11 | -0.11 – 0.32 | 0.334 | 0.334 |
| Random Effects | ||||
| σ2 | 40.90 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.92 | |||
| ICC | 0.14 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.594 / 0.653 | |||
73 MODEL 2: MADRS by NIC_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+NIC_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by NIC_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.35 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.801 | 0.801 |
| sex [female] | 0.17 | -0.00 – 0.34 | 0.053 | 0.053 |
| BMI | 0.02 | -0.06 – 0.11 | 0.576 | 0.576 |
| NIC nM | -0.03 | -0.15 – 0.09 | 0.604 | 0.604 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
| NIC nM * infusionno [1st] | 0.10 | -0.09 – 0.29 | 0.283 | 0.283 |
| NIC nM * infusionno [3rd] | 0.10 | -0.11 – 0.32 | 0.337 | 0.337 |
| Random Effects | ||||
| σ2 | 40.49 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.62 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.657 | |||
74 MODEL 2: MADRS by NIC_nM_LLOD*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+NIC_nM_LLOD*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by NIC_nM_LLOD*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.35 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.801 | 0.801 |
| sex [female] | 0.17 | -0.00 – 0.34 | 0.053 | 0.053 |
| BMI | 0.02 | -0.06 – 0.11 | 0.576 | 0.576 |
| NIC nM LLOD | -0.03 | -0.15 – 0.09 | 0.604 | 0.604 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.65 | <0.001 | <0.001 |
|
NIC nM LLOD * infusionno [1st] |
0.10 | -0.09 – 0.29 | 0.283 | 0.283 |
|
NIC nM LLOD * infusionno [3rd] |
0.10 | -0.11 – 0.32 | 0.337 | 0.337 |
| Random Effects | ||||
| σ2 | 40.49 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.62 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.593 / 0.657 | |||
75 MODEL 2: MADRS by NTA_nM*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+NTA_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by NTA_nM*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.03 | 0.70 – 1.37 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.770 | 0.770 |
| sex [female] | 0.18 | 0.01 – 0.35 | 0.043 | 0.043 |
| BMI | 0.02 | -0.06 – 0.11 | 0.579 | 0.579 |
| NTA nM | -0.05 | -0.18 – 0.08 | 0.420 | 0.420 |
| infusionno [1st] | -1.30 | -1.50 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.64 | <0.001 | <0.001 |
| NTA nM * infusionno [1st] | 0.05 | -0.15 – 0.26 | 0.593 | 0.593 |
| NTA nM * infusionno [3rd] | 0.14 | -0.06 – 0.34 | 0.181 | 0.181 |
| Random Effects | ||||
| σ2 | 40.69 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 8.20 | |||
| ICC | 0.17 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.587 / 0.657 | |||
76 MODEL 2: MADRS by SAA_ng_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+SAA_ng_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## [1] 85 13sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by SAA_ng_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.02 | 0.71 – 1.33 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.10 | 0.823 | 0.823 |
| sex [female] | 0.16 | -0.02 – 0.34 | 0.084 | 0.084 |
| BMI | 0.01 | -0.09 – 0.10 | 0.880 | 0.880 |
| SAA ng mL | 0.01 | -0.14 – 0.16 | 0.867 | 0.867 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | <0.001 | <0.001 |
| infusionno [3rd] | -1.83 | -2.03 – -1.63 | <0.001 | <0.001 |
|
SAA ng mL * infusionno [1st] |
-0.01 | -0.21 – 0.19 | 0.919 | 0.919 |
|
SAA ng mL * infusionno [3rd] |
0.12 | -0.09 – 0.33 | 0.257 | 0.257 |
| Random Effects | ||||
| σ2 | 41.99 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 6.68 | |||
| ICC | 0.14 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.586 / 0.643 | |||
77 MODEL 2: MADRS by VCAM_1_ng_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+VCAM_1_ng_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by VCAM_1_ng_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.01 | 0.69 – 1.34 | <0.001 | <0.001 |
| age | 0.01 | -0.07 – 0.10 | 0.787 | 0.787 |
| sex [female] | 0.19 | 0.01 – 0.36 | 0.039 | 0.039 |
| BMI | 0.03 | -0.06 – 0.11 | 0.537 | 0.537 |
| VCAM 1 ng mL | -0.01 | -0.16 – 0.14 | 0.899 | 0.899 |
| infusionno [1st] | -1.29 | -1.49 – -1.09 | 0.005 | <0.001 |
| infusionno [3rd] | -1.85 | -2.05 – -1.64 | <0.001 | <0.001 |
|
VCAM 1 ng mL * infusionno [1st] |
0.03 | -0.18 – 0.23 | 0.811 | 0.811 |
|
VCAM 1 ng mL * infusionno [3rd] |
0.06 | -0.14 – 0.26 | 0.527 | 0.527 |
| Random Effects | ||||
| σ2 | 41.11 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 7.55 | |||
| ICC | 0.16 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.588 / 0.652 | |||
78 MODEL 2: MADRS by ICAM_1_ng_mL*infusionno
rlmer<-robustlmm::rlmer(MADRS_Score~age+sex+BMI+ICAM_1_ng_mL*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(rlmer))
## 148 85
## 147 85sjPlot::tab_model(rlmer, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS by ICAM_1_ng_mL*infusionno")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 0.98 | 0.68 – 1.28 | <0.001 | <0.001 |
| age | 0.01 | -0.08 – 0.10 | 0.819 | 0.819 |
| sex [female] | 0.20 | 0.03 – 0.37 | 0.023 | 0.023 |
| BMI | 0.01 | -0.07 – 0.10 | 0.776 | 0.776 |
| ICAM 1 ng mL | 0.06 | -0.08 – 0.21 | 0.374 | 0.374 |
| infusionno [1st] | -1.28 | -1.48 – -1.08 | 0.001 | <0.001 |
| infusionno [3rd] | -1.84 | -2.04 – -1.64 | <0.001 | <0.001 |
|
ICAM 1 ng mL * infusionno [1st] |
0.03 | -0.17 – 0.22 | 0.802 | 0.802 |
|
ICAM 1 ng mL * infusionno [3rd] |
0.03 | -0.17 – 0.23 | 0.750 | 0.750 |
| Random Effects | ||||
| σ2 | 40.16 | |||
| τ00 patientno | 0.00 | |||
| τ00 Site_Location | 5.99 | |||
| ICC | 0.13 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.603 / 0.654 | |||
79 MODEL 3: MADRS_Score_3rd by TRP_nM_BL
Biok_wide_new$TRP_nM_BL<-exp(Biok_wide_new$TRP_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+TRP_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 4 25
## 2 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by TRP_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.27 | -0.67 – 0.12 | 0.090 |
| age | 0.08 | -0.17 – 0.32 | 0.536 |
| sex [female] | 0.43 | -0.08 – 0.93 | 0.095 |
| BMI | 0.04 | -0.20 – 0.28 | 0.727 |
| MADRS Score BL | 0.23 | -0.00 – 0.47 | 0.052 |
| TRP nM BL | 0.27 | 0.03 – 0.51 | 0.030 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.152 / 0.086 | ||
80 MODEL 3: MADRS_Score_3rd by five_HT_nM_BL
Biok_wide_new$five_HT_nM_BL<-exp(Biok_wide_new$five_HT_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+five_HT_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by five_HT_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.13 | -0.52 – 0.25 | 0.262 |
| age | 0.10 | -0.14 – 0.35 | 0.404 |
| sex [female] | 0.21 | -0.27 – 0.69 | 0.390 |
| BMI | 0.04 | -0.20 – 0.28 | 0.718 |
| MADRS Score BL | 0.28 | 0.05 – 0.52 | 0.020 |
| five HT nM BL | 0.28 | 0.05 – 0.52 | 0.020 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.161 / 0.095 | ||
81 MODEL 3: MADRS_Score_3rd by KYN_nM_BL
Biok_wide_new$KYN_nM_BL<-exp(Biok_wide_new$KYN_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.23 | -0.63 – 0.17 | 0.356 |
| age | -0.01 | -0.28 – 0.25 | 0.916 |
| sex [female] | 0.36 | -0.15 – 0.86 | 0.165 |
| BMI | -0.01 | -0.25 – 0.24 | 0.965 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.040 |
| KYN nM BL | 0.19 | -0.08 – 0.45 | 0.163 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.114 / 0.045 | ||
82 MODEL 3: MADRS_Score_3rd by three_HK_nM_BL
Biok_wide_new$three_HK_nM_BL<-exp(Biok_wide_new$three_HK_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+three_HK_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by three_HK_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.625 |
| age | 0.05 | -0.21 – 0.30 | 0.702 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.272 |
| BMI | 0.01 | -0.24 – 0.27 | 0.912 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.043 |
| three HK nM BL | 0.02 | -0.23 – 0.26 | 0.895 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.015 | ||
83 MODEL 3: MADRS_Score_3rd by KYNA_nM_BL
Biok_wide_new$KYNA_nM_BL<-exp(Biok_wide_new$KYNA_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYNA_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYNA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.58 – 0.24 | 0.697 |
| age | 0.05 | -0.20 – 0.31 | 0.682 |
| sex [female] | 0.27 | -0.25 – 0.79 | 0.303 |
| BMI | 0.02 | -0.23 – 0.27 | 0.893 |
| MADRS Score BL | 0.25 | 0.00 – 0.50 | 0.050 |
| KYNA nM BL | -0.02 | -0.28 – 0.25 | 0.907 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.015 | ||
84 MODEL 3: MADRS_Score_3rd by PIC_nM_BL
Biok_wide_new$PIC_nM_BL<-exp(Biok_wide_new$PIC_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+PIC_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by PIC_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.697 |
| age | 0.06 | -0.20 – 0.31 | 0.656 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.268 |
| BMI | 0.01 | -0.24 – 0.26 | 0.922 |
| MADRS Score BL | 0.25 | 0.00 – 0.49 | 0.047 |
| PIC nM BL | -0.05 | -0.29 – 0.19 | 0.687 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.089 / 0.018 | ||
85 MODEL 3: MADRS_Score_3rd by Quin_nM_BL
Biok_wide_new$Quin_nM_BL<-exp(Biok_wide_new$Quin_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+Quin_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by Quin_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.12 | -0.53 – 0.29 | 0.904 |
| age | 0.08 | -0.17 – 0.34 | 0.520 |
| sex [female] | 0.19 | -0.33 – 0.71 | 0.465 |
| BMI | 0.03 | -0.22 – 0.28 | 0.802 |
| MADRS Score BL | 0.26 | 0.02 – 0.50 | 0.037 |
| Quin nM BL | -0.14 | -0.40 – 0.12 | 0.282 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.103 / 0.033 | ||
86 MODEL 3: MADRS_Score_3rd by AA_nM_BL
Biok_wide_new$AA_nM_BL<-exp(Biok_wide_new$AA_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+AA_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 25
## 1 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by AA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.59 – 0.18 | 0.680 |
| age | 0.10 | -0.15 – 0.34 | 0.446 |
| sex [female] | 0.31 | -0.17 – 0.80 | 0.201 |
| BMI | 0.10 | -0.15 – 0.35 | 0.446 |
| MADRS Score BL | 0.21 | -0.03 – 0.45 | 0.086 |
| AA nM BL | -0.27 | -0.53 – -0.02 | 0.034 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.149 / 0.082 | ||
87 MODEL 3: MADRS_Score_3rd by KYN_TRP_ratio_BL
Biok_wide_new$KYN_TRP_ratio_BL<-exp(Biok_wide_new$KYN_TRP_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_TRP_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_TRP_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.683 |
| age | 0.06 | -0.21 – 0.33 | 0.670 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.271 |
| BMI | 0.02 | -0.23 – 0.28 | 0.867 |
| MADRS Score BL | 0.25 | 0.00 – 0.50 | 0.046 |
| KYN TRP ratio BL | -0.02 | -0.30 – 0.25 | 0.872 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.016 | ||
88 MODEL 3: MADRS_Score_3rd by KYN_SER_ratio_BL
Biok_wide_new$KYN_SER_ratio_BL<-exp(Biok_wide_new$KYN_SER_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_SER_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_SER_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.20 | 0.477 |
| age | 0.07 | -0.18 – 0.32 | 0.574 |
| sex [female] | 0.31 | -0.19 – 0.81 | 0.221 |
| BMI | 0.02 | -0.23 – 0.26 | 0.895 |
| MADRS Score BL | 0.30 | 0.04 – 0.55 | 0.022 |
| KYN SER ratio BL | -0.15 | -0.40 – 0.10 | 0.239 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.106 / 0.036 | ||
89 MODEL 3: MADRS_Score_3rd by QUIN_PIC_ratio_BL
Biok_wide_new$QUIN_PIC_ratio_BL<-exp(Biok_wide_new$QUIN_PIC_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+QUIN_PIC_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by QUIN_PIC_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.59 – 0.20 | 0.579 |
| age | 0.07 | -0.19 – 0.32 | 0.590 |
| sex [female] | 0.30 | -0.19 – 0.80 | 0.228 |
| BMI | 0.05 | -0.21 – 0.30 | 0.709 |
| MADRS Score BL | 0.28 | 0.03 – 0.52 | 0.029 |
| QUIN PIC ratio BL | -0.13 | -0.38 – 0.12 | 0.300 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.102 / 0.032 | ||
90 MODEL 3: MADRS_Score_3rd by QUIN_KYNA_ratio_BL
Biok_wide_new$QUIN_KYNA_ratio_BL<-exp(Biok_wide_new$QUIN_KYNA_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+QUIN_KYNA_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by QUIN_KYNA_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.660 |
| age | 0.05 | -0.20 – 0.31 | 0.680 |
| sex [female] | 0.30 | -0.20 – 0.80 | 0.240 |
| BMI | 0.03 | -0.22 – 0.27 | 0.839 |
| MADRS Score BL | 0.27 | 0.02 – 0.53 | 0.034 |
| QUIN KYNA ratio BL | -0.08 | -0.33 – 0.17 | 0.536 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.092 / 0.021 | ||
91 MODEL 3: MADRS_Score_3rd by threeHK_KYN_ratio_BL
Biok_wide_new$threeHK_KYN_ratio_BL<-exp(Biok_wide_new$threeHK_KYN_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+threeHK_KYN_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by threeHK_KYN_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.20 | 0.934 |
| age | 0.03 | -0.22 – 0.29 | 0.788 |
| sex [female] | 0.31 | -0.19 – 0.81 | 0.226 |
| BMI | 0.04 | -0.21 – 0.29 | 0.776 |
| MADRS Score BL | 0.23 | -0.01 – 0.48 | 0.062 |
| threeHK KYN ratio BL | -0.11 | -0.36 – 0.13 | 0.360 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
92 MODEL 3: MADRS_Score_3rd by threeHK_KYNA_ratio_BL
Biok_wide_new$threeHK_KYNA_ratio_BL<-exp(Biok_wide_new$threeHK_KYNA_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+threeHK_KYNA_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 25
## 1 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by threeHK_KYNA_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.14 | -0.54 – 0.27 | 0.629 |
| age | 0.05 | -0.20 – 0.30 | 0.680 |
| sex [female] | 0.21 | -0.31 – 0.73 | 0.416 |
| BMI | -0.01 | -0.26 – 0.24 | 0.943 |
| MADRS Score BL | 0.24 | -0.00 – 0.49 | 0.051 |
| threeHK KYNA ratio BL | 0.12 | -0.14 – 0.37 | 0.362 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
93 MODEL 3: MADRS_Score_3rd by IL1B_pg_mL_BL
Biok_wide_new$IL1B_pg_mL_BL<-exp(Biok_wide_new$IL1B_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL1B_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 19 63
## 2 13sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL1B_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | 0.42 | -0.49 – 1.33 | 0.809 |
| age | -0.21 | -0.94 – 0.53 | 0.548 |
| sex [female] | -0.83 | -2.30 – 0.64 | 0.235 |
| BMI | -0.04 | -0.77 – 0.69 | 0.898 |
| MADRS Score BL | 0.50 | -0.44 – 1.43 | 0.263 |
| IL1B pg mL BL | 0.43 | -0.20 – 1.06 | 0.158 |
| Observations | 16 | ||
| R2 / R2 adjusted | 0.386 / 0.079 | ||
94 MODEL 3: MADRS_Score_3rd by IL1B_pg_mL_LLOD_BL
Biok_wide_new$IL1B_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL1B_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL1B_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL1B_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.568 |
| age | 0.05 | -0.20 – 0.30 | 0.692 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.292 |
| BMI | 0.02 | -0.23 – 0.27 | 0.880 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL1B pg mL LLOD BL | 0.05 | -0.19 – 0.29 | 0.698 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.089 / 0.018 | ||
95 MODEL 3: MADRS_Score_3rd by IL2_pg_mL_BL
Biok_wide_new$IL2_pg_mL_BL<-exp(Biok_wide_new$IL2_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL2_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 22
## 1 13sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL2_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | 0.14 | -0.37 – 0.65 | 0.895 |
| age | 0.19 | -0.13 – 0.51 | 0.242 |
| sex [female] | -0.21 | -0.85 – 0.42 | 0.499 |
| BMI | -0.10 | -0.41 – 0.21 | 0.505 |
| MADRS Score BL | 0.33 | 0.02 – 0.64 | 0.036 |
| IL2 pg mL BL | 0.27 | -0.04 – 0.57 | 0.087 |
| Observations | 44 | ||
| R2 / R2 adjusted | 0.195 / 0.089 | ||
96 MODEL 3: MADRS_Score_3rd by IL2_pg_mL_LLOD_BL
Biok_wide_new$IL2_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL2_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL2_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL2_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.56 – 0.23 | 0.593 |
| age | 0.07 | -0.19 – 0.32 | 0.603 |
| sex [female] | 0.26 | -0.24 – 0.75 | 0.305 |
| BMI | 0.03 | -0.22 – 0.27 | 0.819 |
| MADRS Score BL | 0.24 | -0.00 – 0.48 | 0.054 |
| IL2 pg mL LLOD BL | 0.16 | -0.09 – 0.40 | 0.201 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.110 / 0.040 | ||
97 MODEL 3: MADRS_Score_3rd by IL4_pg_mL_BL
Biok_wide_new$IL4_pg_mL_BL<-exp(Biok_wide_new$IL4_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL4_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL4_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.515 |
| age | 0.07 | -0.19 – 0.33 | 0.606 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.293 |
| BMI | 0.00 | -0.25 – 0.26 | 0.969 |
| MADRS Score BL | 0.27 | 0.02 – 0.52 | 0.037 |
| IL4 pg mL BL | 0.06 | -0.19 – 0.32 | 0.622 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
98 MODEL 3: MADRS_Score_3rd by IL4_pg_mL_LLOD_BL
Biok_wide_new$IL4_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL4_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL4_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL4_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.515 |
| age | 0.07 | -0.19 – 0.33 | 0.606 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.293 |
| BMI | 0.00 | -0.25 – 0.26 | 0.969 |
| MADRS Score BL | 0.27 | 0.02 – 0.52 | 0.037 |
| IL4 pg mL LLOD BL | 0.06 | -0.19 – 0.32 | 0.622 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
99 MODEL 3: MADRS_Score_3rd by IL6_pg_mL_BL
Biok_wide_new$IL6_pg_mL_BL<-exp(Biok_wide_new$IL6_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL6_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL6_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.61 – 0.20 | 0.553 |
| age | 0.06 | -0.19 – 0.32 | 0.624 |
| sex [female] | 0.31 | -0.20 – 0.83 | 0.226 |
| BMI | 0.05 | -0.22 – 0.32 | 0.716 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.036 |
| IL6 pg mL BL | -0.08 | -0.36 – 0.19 | 0.539 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.092 / 0.021 | ||
100 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
101 MODEL 3: MADRS_Score_3rd by IL10_pg_mL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
102 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
103 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
104 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
105 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
106 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
107 MODEL 3: MADRS_Score_3rd by IL10_pg_mL_BL
Biok_wide_new$IL10_pg_mL_BL<-exp(Biok_wide_new$IL10_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL10_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL10_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.21 | -0.60 – 0.19 | 0.634 |
| age | 0.07 | -0.18 – 0.32 | 0.590 |
| sex [female] | 0.32 | -0.17 – 0.82 | 0.200 |
| BMI | 0.01 | -0.23 – 0.26 | 0.914 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.043 |
| IL10 pg mL BL | -0.18 | -0.42 – 0.06 | 0.137 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.118 / 0.049 | ||
108 MODEL 3: MADRS_Score_3rd by IL12p70_pg_mL_BL
Biok_wide_new$IL12p70_pg_mL_BL<-exp(Biok_wide_new$IL12p70_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL12p70_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL12p70_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.599 |
| age | 0.07 | -0.19 – 0.33 | 0.612 |
| sex [female] | 0.30 | -0.21 – 0.81 | 0.243 |
| BMI | 0.00 | -0.25 – 0.26 | 0.972 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.038 |
| IL12p70 pg mL BL | 0.07 | -0.19 – 0.32 | 0.604 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
109 MODEL 3: MADRS_Score_3rd by IL12p70_pg_mL_LLOD_BL
Biok_wide_new$IL12p70_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL12p70_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL12p70_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL12p70_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.599 |
| age | 0.07 | -0.19 – 0.33 | 0.612 |
| sex [female] | 0.30 | -0.21 – 0.81 | 0.243 |
| BMI | 0.00 | -0.25 – 0.26 | 0.972 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.038 |
| IL12p70 pg mL LLOD BL | 0.07 | -0.19 – 0.32 | 0.604 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
110 MODEL 3: MADRS_Score_3rd by IL13_pg_mL_BL
Biok_wide_new$IL13_pg_mL_BL<-exp(Biok_wide_new$IL13_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL13_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL13_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.59 – 0.24 | 0.645 |
| age | 0.06 | -0.20 – 0.33 | 0.627 |
| sex [female] | 0.27 | -0.25 – 0.78 | 0.304 |
| BMI | 0.00 | -0.25 – 0.26 | 0.978 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.044 |
| IL13 pg mL BL | 0.06 | -0.20 – 0.31 | 0.650 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.082 / 0.009 | ||
111 MODEL 3: MADRS_Score_3rd by IL13_pg_mL_LLOD_BL
Biok_wide_new$IL13_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL13_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL13_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL13_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.585 |
| age | 0.07 | -0.19 – 0.33 | 0.607 |
| sex [female] | 0.30 | -0.21 – 0.80 | 0.244 |
| BMI | 0.00 | -0.25 – 0.26 | 0.976 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| IL13 pg mL LLOD BL | 0.07 | -0.19 – 0.32 | 0.605 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
112 MODEL 3: MADRS_Score_3rd by TNFa_pg_mL_BL
Biok_wide_new$TNFa_pg_mL_BL<-exp(Biok_wide_new$TNFa_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+TNFa_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by TNFa_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.22 | 0.588 |
| age | 0.05 | -0.21 – 0.30 | 0.714 |
| sex [female] | 0.29 | -0.22 – 0.81 | 0.259 |
| BMI | 0.02 | -0.23 – 0.26 | 0.898 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.042 |
| TNFa pg mL BL | 0.03 | -0.22 – 0.29 | 0.793 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
113 MODEL 3: MADRS_Score_3rd by IFNy_pg_mL_BL
Biok_wide_new$IFNy_pg_mL_BL<-exp(Biok_wide_new$IFNy_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IFNy_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IFNy_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.699 |
| age | 0.06 | -0.20 – 0.31 | 0.657 |
| sex [female] | 0.27 | -0.23 – 0.76 | 0.292 |
| BMI | -0.00 | -0.25 – 0.25 | 0.997 |
| MADRS Score BL | 0.28 | 0.03 – 0.53 | 0.029 |
| IFNy pg mL BL | -0.11 | -0.36 – 0.13 | 0.368 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
114 MODEL 3: MADRS_Score_3rd by CRP_ng_mL_BL
Biok_wide_new$CRP_ng_mL_BL<-exp(Biok_wide_new$CRP_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+CRP_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by CRP_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.16 | -0.56 – 0.24 | 0.797 |
| age | 0.06 | -0.20 – 0.31 | 0.660 |
| sex [female] | 0.25 | -0.25 – 0.75 | 0.321 |
| BMI | -0.03 | -0.30 – 0.23 | 0.805 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.045 |
| CRP ng mL BL | 0.13 | -0.13 – 0.38 | 0.335 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.100 / 0.029 | ||
115 MODEL 3: MADRS_Score_3rd by NIC_nM_BL
Biok_wide_new$NIC_nM_BL<-exp(Biok_wide_new$NIC_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NIC_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NIC_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.619 |
| age | 0.05 | -0.20 – 0.30 | 0.685 |
| sex [female] | 0.26 | -0.24 – 0.76 | 0.301 |
| BMI | 0.01 | -0.23 – 0.26 | 0.915 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| NIC nM BL | 0.07 | -0.17 – 0.31 | 0.568 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.091 / 0.020 | ||
116 MODEL 3: MADRS_Score_3rd by NIC_nM_LLOD_BL
Biok_wide_new$NIC_nM_LLOD_BL<-exp(Biok_wide_new$NIC_nM_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NIC_nM_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NIC_nM_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.619 |
| age | 0.05 | -0.20 – 0.30 | 0.685 |
| sex [female] | 0.26 | -0.24 – 0.76 | 0.301 |
| BMI | 0.01 | -0.23 – 0.26 | 0.915 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| NIC nM LLOD BL | 0.07 | -0.17 – 0.31 | 0.568 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.091 / 0.020 | ||
117 MODEL 3: MADRS_Score_3rd by NTA_nM_BL
Biok_wide_new$NTA_nM_BL<-exp(Biok_wide_new$NTA_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NTA_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NTA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.21 | 0.472 |
| age | 0.07 | -0.19 – 0.32 | 0.612 |
| sex [female] | 0.30 | -0.20 – 0.81 | 0.234 |
| BMI | 0.02 | -0.22 – 0.27 | 0.846 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.037 |
| NTA nM BL | 0.08 | -0.16 – 0.33 | 0.500 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.093 / 0.022 | ||
118 MODEL 3: MADRS_Score_3rd by SAA_ng_mL_BL
Biok_wide_new$SAA_ng_mL_BL<-exp(Biok_wide_new$SAA_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+SAA_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by SAA_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.16 | -0.57 – 0.24 | 0.722 |
| age | 0.05 | -0.21 – 0.30 | 0.708 |
| sex [female] | 0.25 | -0.26 – 0.76 | 0.333 |
| BMI | -0.01 | -0.28 – 0.26 | 0.938 |
| MADRS Score BL | 0.25 | 0.00 – 0.49 | 0.048 |
| SAA ng mL BL | 0.07 | -0.20 – 0.34 | 0.616 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
119 MODEL 3: MADRS_Score_3rd by VCAM_1_ng_mL_BL
Biok_wide_new$VCAM_1_ng_mL_BL<-exp(Biok_wide_new$VCAM_1_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+VCAM_1_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by VCAM_1_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.24 | 0.869 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.26 | -0.25 – 0.77 | 0.312 |
| BMI | 0.02 | -0.23 – 0.27 | 0.882 |
| MADRS Score BL | 0.25 | -0.00 – 0.49 | 0.052 |
| VCAM 1 ng mL BL | -0.04 | -0.29 – 0.21 | 0.763 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.017 | ||
120 MODEL 3: MADRS_Score_3rd by ICAM_1_ng_mL_BL
Biok_wide_new$ICAM_1_ng_mL_BL<-exp(Biok_wide_new$ICAM_1_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+ICAM_1_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by ICAM_1_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.21 | -0.61 – 0.19 | 0.440 |
| age | 0.04 | -0.21 – 0.29 | 0.738 |
| sex [female] | 0.32 | -0.18 – 0.82 | 0.208 |
| BMI | 0.01 | -0.23 – 0.26 | 0.912 |
| MADRS Score BL | 0.23 | -0.02 – 0.47 | 0.074 |
| ICAM 1 ng mL BL | 0.13 | -0.12 – 0.37 | 0.307 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.101 / 0.031 | ||