Biok study: biomarker analysis
Stephen Murata
1/15/2022
## [1] 218 651 Sample characteristics (all raw variables) by Remission
compareGroups::descrTable(Remission~.
, Biok_vert_df_raw,
hide.no = '0',
show.p.overall = FALSE,
include.label = TRUE)##
## --------Summary descriptives table by 'Remission'---------
##
## ______________________________________________________________________
## No remission Remitter
## N=77 N=138
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex:
## male 23 (29.9%) 56 (40.6%)
## female 54 (70.1%) 82 (59.4%)
## age 43.7 (13.9) 44.5 (12.4)
## BMI 28.6 (6.46) 28.7 (5.01)
## race:
## asian 0 (0.00%) 3 (2.17%)
## black 3 (3.90%) 1 (0.72%)
## white 74 (96.1%) 134 (97.1%)
## patientno 1000 (1365) 1045 (1377)
## Site_Location:
## Johns Hopkins 7 (9.09%) 15 (10.9%)
## Univeristy of Michigan 24 (31.2%) 45 (32.6%)
## Mayo Clinic 39 (50.6%) 72 (52.2%)
## Pine Rest 7 (9.09%) 6 (4.35%)
## infusionno:
## BL 26 (33.8%) 46 (33.3%)
## 1st 25 (32.5%) 46 (33.3%)
## 3rd 26 (33.8%) 46 (33.3%)
## Blood_Draw_Event:
## Acute Infusion #1 Baseline 100 18 (23.4%) 30 (21.7%)
## Acute Infusion #1 Baseline 40 8 (10.4%) 16 (11.6%)
## Acute Infusion #1 Stop 100 17 (22.1%) 30 (21.7%)
## Acute Infusion #1 Stop 40 8 (10.4%) 16 (11.6%)
## Acute Infusion #3 Stop 100 11 (14.3%) 16 (11.6%)
## Acute Infusion #3 Stop 40 15 (19.5%) 30 (21.7%)
## Batch_Number 3.34 (1.74) 3.62 (1.71)
## BSS_Score 6.51 (7.29) 3.43 (6.12)
## MADRS_Score 21.8 (8.46) 14.4 (10.8)
## TRP_nM 26255 (5646) 25152 (6045)
## five_HT_nM 302 (362) 154 (215)
## KYN_nM 940 (238) 914 (244)
## three_HK_nM 16.1 (6.15) 16.4 (8.09)
## KYNA_nM 19.0 (7.09) 20.0 (7.80)
## PIC_nM 19.2 (9.98) 19.3 (15.2)
## Quin_nM 141 (39.6) 150 (44.5)
## AA_nM 5.28 (1.79) 6.39 (3.62)
## KYN_TRP_ratio 0.04 (0.01) 0.04 (0.01)
## KYN_SER_ratio 30.6 (74.7) 34.8 (71.7)
## QUIN_PIC_ratio 8.57 (3.64) 9.50 (4.77)
## QUIN_KYNA_ratio 8.20 (3.51) 8.16 (3.07)
## threeHK_KYN_ratio 0.02 (0.00) 0.02 (0.01)
## threeHK_KYNA_ratio 0.95 (0.53) 0.88 (0.37)
## IL1B_pg_mL 0.32 (0.43) 0.26 (0.48)
## IL1B_pg_mL_LLOD 0.34 (0.15) 0.34 (0.21)
## IL2_pg_mL 2.89 (10.1) 0.43 (0.27)
## IL2_pg_mL_LLOD 1.98 (8.39) 0.34 (0.27)
## IL4_pg_mL 0.09 (0.03) 0.09 (0.03)
## IL4_pg_mL_LLOD 0.09 (0.03) 0.09 (0.03)
## IL6_pg_mL 1.13 (0.69) 1.00 (0.51)
## IL8_pg_mL 4.18 (1.82) 4.48 (1.57)
## IL10_pg_mL 0.39 (0.14) 0.78 (2.44)
## IL12p70_pg_mL 0.46 (0.25) 34.3 (233)
## IL12p70_pg_mL_LLOD 0.46 (0.25) 34.0 (232)
## IL13_pg_mL 3.62 (0.74) 5.93 (14.5)
## IL13_pg_mL_LLOD 3.62 (0.74) 5.81 (14.3)
## TNFa_pg_mL 1.37 (0.28) 1.48 (0.54)
## IFNy_pg_mL 6.78 (5.63) 7.12 (5.49)
## CRP_ng_mL 4004 (6567) 2141 (3292)
## NIC_nM 113 (568) 2.29 (5.43)
## NIC_nM_LLOD 113 (568) 2.29 (5.43)
## NTA_nM 297 (152) 313 (155)
## SAA_ng_mL 3187 (2304) 2699 (2063)
## VCAM_1_ng_mL 310 (68.5) 309 (68.4)
## ICAM_1_ng_mL 325 (86.5) 300 (79.3)
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯2 Sample characteristics (biomarkers only) in whole group
compareGroups::descrTable(~.
, Biok_biomarkers_df,
hide.no = '0',
show.p.overall = FALSE,
include.label = TRUE)##
## --------Summary descriptives table ---------
##
## ___________________________________
## [ALL] N
## N=218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## 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
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯3 Sample missingness counts by variable
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%) .
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯4 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()
ggplot(Biok_vert_df, aes(x=MADRS_Score,fill=Remission))+
geom_histogram(color="black")+
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)
5 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)
6 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$P,
insig="blank",
order="hclust",
method="color",
type="upper",
diag=FALSE,
number.cex=0.8,
na.label.col = "gray",
addgrid.col=TRUE,
tl.col = 'red',
tl.srt = 45,
title="Pearson's matrix of age, BMI, and log transformed biomarkers (whole sample)")
7 Model 1 (mixed effects): TRP by Remission*infusionno
rlmer_TRP<-robustlmm::rlmer(TRP_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_TRP_vif<-lme4::lmer(TRP_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_TRP_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032645 1 1.016192
## age 1.090413 1 1.044228
## BMI 1.077056 1 1.037813
## Remission 1.011650 1 1.005808
## infusionno 1.000665 2 1.000166car::qqPlot(residuals(rlmer_TRP), main="QQ-PLOT")
## 218 42
## 215 41pairwise_remission<-emmeans(rlmer_TRP, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1360 1348 Inf 1.009 0.3131
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 439 1358 Inf 0.323 0.7465
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1730 1349 Inf 1.282 0.1999
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer_TRP, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 3900 980 Inf 3.980 0.0002
## BL - 3rd 3081 967 Inf 3.188 0.0041
## 1st - 3rd -819 970 Inf -0.844 0.6758
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2979 724 Inf 4.117 0.0001
## BL - 3rd 3451 724 Inf 4.769 <.0001
## 1st - 3rd 472 720 Inf 0.656 0.7891
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates#plotting
pairwise_infusionno<-emmeans(rlmer_TRP, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="Tryptophan (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer_TRP, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="Tryptophan (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
8 MODEL 1: five_HT_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(five_HT_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(five_HT_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033444 1 1.016584
## age 1.092212 1 1.045090
## BMI 1.078296 1 1.038410
## Remission 1.011798 1 1.005882
## infusionno 1.000460 2 1.000115car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 8 171
## 7 168pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 125.7 47.4 Inf 2.650 0.0080
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 74.8 47.5 Inf 1.574 0.1155
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 133.5 47.4 Inf 2.815 0.0049
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 58.6 13.41 Inf 4.366 <.0001
## BL - 3rd 18.6 13.21 Inf 1.411 0.3351
## 1st - 3rd -39.9 13.22 Inf -3.019 0.0072
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 7.6 9.87 Inf 0.770 0.7215
## BL - 3rd 26.5 9.87 Inf 2.683 0.0200
## 1st - 3rd 18.9 9.79 Inf 1.928 0.1307
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="five_HT_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="five_HT_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
# MODEL 1: KYN_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033090 1 1.016410
## age 1.091077 1 1.044546
## BMI 1.077211 1 1.037888
## Remission 1.011730 1 1.005848
## infusionno 1.000464 2 1.000116car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 218 42
## 215 41pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 57.5 51.1 Inf 1.125 0.2605
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 35.3 51.3 Inf 0.687 0.4920
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 53.1 51.1 Inf 1.038 0.2992
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 98.28 27.1 Inf 3.633 0.0008
## BL - 3rd 86.02 26.7 Inf 3.226 0.0036
## 1st - 3rd -12.26 26.7 Inf -0.459 0.8905
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 76.05 19.9 Inf 3.814 0.0004
## BL - 3rd 81.62 19.9 Inf 4.093 0.0001
## 1st - 3rd 5.57 19.8 Inf 0.281 0.9574
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="KYN_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="KYN_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
# MODEL 1: three_HK_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(three_HK_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(three_HK_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033604 1 1.016663
## age 1.092267 1 1.045116
## BMI 1.078036 1 1.038285
## Remission 1.011810 1 1.005888
## infusionno 1.000328 2 1.000082car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 55
## 41 54pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.425 1.14 Inf 0.373 0.7091
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.425 1.14 Inf 0.371 0.7104
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.527 1.14 Inf 0.462 0.6438
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.8887 0.597 Inf 1.488 0.2964
## BL - 3rd 0.9013 0.589 Inf 1.531 0.2761
## 1st - 3rd 0.0126 0.590 Inf 0.021 0.9997
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.8886 0.440 Inf 2.019 0.1076
## BL - 3rd 1.0035 0.440 Inf 2.280 0.0586
## 1st - 3rd 0.1149 0.437 Inf 0.263 0.9626
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="three_HK_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="three_HK_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
9 MODEL 1: KYNA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(KYNA_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYNA_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033107 1 1.016419
## age 1.092020 1 1.044998
## BMI 1.078786 1 1.038646
## Remission 1.011785 1 1.005875
## infusionno 1.000717 2 1.000179car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 218
## 41 215pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.125 1.63 Inf -0.077 0.9390
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.609 1.64 Inf -0.371 0.7105
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.406 1.63 Inf -0.248 0.8038
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.347 1.012 Inf 1.331 0.3776
## BL - 3rd 1.569 0.998 Inf 1.572 0.2578
## 1st - 3rd 0.221 1.001 Inf 0.221 0.9734
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.863 0.746 Inf 1.156 0.4795
## BL - 3rd 1.288 0.746 Inf 1.725 0.1958
## 1st - 3rd 0.424 0.742 Inf 0.572 0.8349
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="KYNA_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="KYNA_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
# MODEL 1: PIC_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(PIC_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(PIC_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032491 1 1.016116
## age 1.090598 1 1.044317
## BMI 1.078025 1 1.038280
## Remission 1.011718 1 1.005842
## infusionno 1.000921 2 1.000230car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 42 159
## 41 156pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.330 1.80 Inf 0.183 0.8548
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.214 1.81 Inf 0.118 0.9059
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.017 1.80 Inf -0.009 0.9925
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.401 1.026 Inf 2.339 0.0506
## BL - 3rd 1.729 1.012 Inf 1.709 0.2018
## 1st - 3rd -0.672 1.014 Inf -0.662 0.7855
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.285 0.757 Inf 3.020 0.0071
## BL - 3rd 1.382 0.757 Inf 1.827 0.1609
## 1st - 3rd -0.903 0.752 Inf -1.201 0.4527
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="PIC_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="PIC_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
10 MODEL 1: Quin_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(Quin_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(Quin_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033074 1 1.016403
## age 1.090809 1 1.044418
## BMI 1.076727 1 1.037655
## Remission 1.011701 1 1.005833
## infusionno 1.000373 2 1.000093car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 61 218
## 60 215pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -2.58 10.2 Inf -0.253 0.8003
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.57 10.2 Inf -0.154 0.8778
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -4.75 10.2 Inf -0.466 0.6414
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.589 4.53 Inf 0.351 0.9344
## BL - 3rd 0.982 4.46 Inf 0.220 0.9737
## 1st - 3rd -0.607 4.47 Inf -0.136 0.9899
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.594 3.34 Inf 0.778 0.7167
## BL - 3rd -1.192 3.34 Inf -0.357 0.9321
## 1st - 3rd -3.786 3.31 Inf -1.143 0.4874
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="Quin_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="Quin_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
# MODEL 1: AA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(AA_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(AA_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033188 1 1.016459
## age 1.092077 1 1.045025
## BMI 1.078677 1 1.038594
## Remission 1.011786 1 1.005876
## infusionno 1.000658 2 1.000164car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 50 44
## 49 43pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.146 0.519 Inf -2.210 0.0271
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.454 0.523 Inf -0.868 0.3852
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.131 0.519 Inf -0.253 0.8002
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.372 0.412 Inf -0.903 0.6382
## BL - 3rd -0.164 0.407 Inf -0.403 0.9142
## 1st - 3rd 0.208 0.409 Inf 0.510 0.8666
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.320 0.305 Inf 1.049 0.5459
## BL - 3rd 0.851 0.305 Inf 2.793 0.0145
## 1st - 3rd 0.531 0.303 Inf 1.752 0.1859
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="Anthranilic acid (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="Anthranilic acid (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
11 MODEL 1: KYN_TRP_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_TRP_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_TRP_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033551 1 1.016637
## age 1.092251 1 1.045108
## BMI 1.078124 1 1.038327
## Remission 1.011806 1 1.005886
## infusionno 1.000372 2 1.000093car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 162 55
## 159 54pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.001088 0.00207 Inf 0.525 0.5995
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.001543 0.00208 Inf 0.741 0.4585
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.000507 0.00207 Inf 0.245 0.8067
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001543 0.001102 Inf -1.401 0.3405
## BL - 3rd -0.001024 0.001086 Inf -0.943 0.6131
## 1st - 3rd 0.000519 0.001088 Inf 0.477 0.8821
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001088 0.000812 Inf -1.339 0.3732
## BL - 3rd -0.001604 0.000812 Inf -1.976 0.1182
## 1st - 3rd -0.000517 0.000806 Inf -0.641 0.7976
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="KYN_TRP_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="KYN_TRP_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
12 MODEL 1: KYN_SER_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(KYN_SER_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(KYN_SER_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.030730 1 1.015249
## age 1.088861 1 1.043485
## BMI 1.075925 1 1.037268
## Remission 1.010702 1 1.005337
## infusionno 1.000980 2 1.000245car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 177 28
## 174 27pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -5.43 4.4 Inf -1.234 0.2171
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -6.51 4.4 Inf -1.478 0.1395
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -8.88 4.4 Inf -2.020 0.0434
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.139 1.54 Inf -0.740 0.7397
## BL - 3rd -0.719 1.52 Inf -0.474 0.8834
## 1st - 3rd 0.419 1.52 Inf 0.276 0.9588
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -2.220 1.13 Inf -1.959 0.1224
## BL - 3rd -4.172 1.13 Inf -3.682 0.0007
## 1st - 3rd -1.952 1.12 Inf -1.736 0.1917
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="KYN_SER_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="KYN_SER_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
13 MODEL 1: QUIN_PIC_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(QUIN_PIC_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(QUIN_PIC_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032809 1 1.016272
## age 1.090248 1 1.044149
## BMI 1.076116 1 1.037360
## Remission 1.011594 1 1.005780
## infusionno 1.000363 2 1.000091car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 152 134
## 149 132pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.491 0.974 Inf -0.504 0.6145
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.405 0.978 Inf -0.414 0.6788
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.444 0.975 Inf -0.455 0.6489
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.899 0.433 Inf -2.077 0.0946
## BL - 3rd -0.714 0.426 Inf -1.675 0.2150
## 1st - 3rd 0.185 0.427 Inf 0.433 0.9020
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.813 0.319 Inf -2.550 0.0290
## BL - 3rd -0.667 0.319 Inf -2.093 0.0913
## 1st - 3rd 0.146 0.316 Inf 0.460 0.8898
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="QUIN_PIC_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="QUIN_PIC_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
14 MODEL 1: QUIN_KYNA_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(QUIN_KYNA_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(QUIN_KYNA_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032218 1 1.015981
## age 1.089631 1 1.043854
## BMI 1.075665 1 1.037143
## Remission 1.011344 1 1.005656
## infusionno 1.000477 2 1.000119car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 152 134
## 149 132pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.1186 0.696 Inf 0.171 0.8646
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0476 0.698 Inf 0.068 0.9456
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0156 0.696 Inf -0.022 0.9821
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.207 0.352 Inf -0.589 0.8262
## BL - 3rd -0.379 0.347 Inf -1.091 0.5194
## 1st - 3rd -0.171 0.348 Inf -0.493 0.8745
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.278 0.259 Inf -1.073 0.5311
## BL - 3rd -0.513 0.259 Inf -1.977 0.1178
## 1st - 3rd -0.235 0.258 Inf -0.911 0.6333
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="QUIN_KYNA_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="QUIN_KYNA_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
15 MODEL 1: threeHK_KYN_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(threeHK_KYN_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(threeHK_KYN_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033677 1 1.016699
## age 1.092216 1 1.045091
## BMI 1.077804 1 1.038173
## Remission 1.011816 1 1.005891
## infusionno 1.000246 2 1.000061car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 217 42
## 214 41pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000665 0.00105 Inf -0.635 0.5253
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000191 0.00105 Inf -0.181 0.8561
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.000448 0.00105 Inf -0.427 0.6693
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -8.41e-04 0.000542 Inf -1.551 0.2672
## BL - 3rd -3.04e-04 0.000534 Inf -0.570 0.8363
## 1st - 3rd 5.36e-04 0.000535 Inf 1.002 0.5756
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -3.66e-04 0.000399 Inf -0.916 0.6299
## BL - 3rd -8.67e-05 0.000399 Inf -0.217 0.9743
## 1st - 3rd 2.79e-04 0.000397 Inf 0.704 0.7610
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="threeHK_KYN_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="threeHK_KYN_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
16 MODEL 1: threeHK_KYNA_ratio by Remission*infusionno
rlmer<-robustlmm::rlmer(threeHK_KYNA_ratio~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(threeHK_KYNA_ratio~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032347 1 1.016045
## age 1.089616 1 1.043847
## BMI 1.075164 1 1.036901
## Remission 1.011318 1 1.005643
## infusionno 1.000270 2 1.000068car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 4 152
## 3 149pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.01923 0.0767 Inf 0.251 0.8019
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.03070 0.0769 Inf 0.399 0.6899
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00393 0.0767 Inf 0.051 0.9591
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01067 0.0348 Inf 0.306 0.9496
## BL - 3rd 0.02405 0.0343 Inf 0.700 0.7632
## 1st - 3rd 0.01338 0.0344 Inf 0.389 0.9199
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.02214 0.0257 Inf 0.862 0.6641
## BL - 3rd 0.00875 0.0257 Inf 0.341 0.9379
## 1st - 3rd -0.01338 0.0255 Inf -0.525 0.8590
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="threeHK_KYNA_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="threeHK_KYNA_ratio (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
17 MODEL 1: IL1B_pg_mL by Remission*infusionno
18 MODEL 1: IL1B_pg_mL_LLOD by Remission*infusionno
19 MODEL 1: IL2_pg_mL by Remission*infusionno
20 MODEL 1: IL2_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL2_pg_mL_LLOD~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL2_pg_mL_LLOD~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032226 1 1.015985
## age 1.089336 1 1.043713
## BMI 1.074250 1 1.036461
## Remission 1.011135 1 1.005552
## infusionno 1.000004 2 1.000001car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 80 8
## 78 7pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.00456 0.0594 Inf -0.077 0.9388
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.01526 0.0596 Inf -0.256 0.7979
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00352 0.0595 Inf 0.059 0.9528
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01779 0.0252 Inf 0.707 0.7594
## BL - 3rd -0.02924 0.0248 Inf -1.179 0.4655
## 1st - 3rd -0.04703 0.0248 Inf -1.894 0.1403
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.00709 0.0185 Inf 0.383 0.9224
## BL - 3rd -0.02116 0.0185 Inf -1.142 0.4884
## 1st - 3rd -0.02825 0.0184 Inf -1.536 0.2742
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates21 MODEL 1: IL4_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL4_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL4_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032263 1 1.016003
## age 1.090430 1 1.044237
## BMI 1.075503 1 1.037065
## Remission 1.011060 1 1.005515
## infusionno 1.000381 2 1.000095car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 45 120
## 44 117pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00262 0.00640 Inf 0.409 0.6822
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00386 0.00644 Inf 0.599 0.5490
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00108 0.00641 Inf 0.169 0.8661
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001962 0.00348 Inf -0.563 0.8397
## BL - 3rd -0.000215 0.00343 Inf -0.063 0.9978
## 1st - 3rd 0.001747 0.00344 Inf 0.508 0.8676
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.000725 0.00259 Inf -0.280 0.9576
## BL - 3rd -0.001757 0.00257 Inf -0.684 0.7727
## 1st - 3rd -0.001032 0.00257 Inf -0.401 0.9151
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates22 MODEL 1: IL4_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL4_pg_mL_LLOD~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL4_pg_mL_LLOD~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032011 1 1.015879
## age 1.089454 1 1.043769
## BMI 1.075218 1 1.036927
## Remission 1.011184 1 1.005576
## infusionno 1.000394 2 1.000098car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 101 120
## 99 118pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00265 0.00644 Inf 0.411 0.6811
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00456 0.00647 Inf 0.705 0.4810
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00119 0.00644 Inf 0.185 0.8532
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.97e-03 0.00353 Inf -0.558 0.8426
## BL - 3rd -2.44e-04 0.00348 Inf -0.070 0.9973
## 1st - 3rd 1.73e-03 0.00349 Inf 0.495 0.8739
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -5.95e-05 0.00260 Inf -0.023 0.9997
## BL - 3rd -1.70e-03 0.00260 Inf -0.653 0.7908
## 1st - 3rd -1.64e-03 0.00259 Inf -0.634 0.8013
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatesBiok_biomarkers## [1] "TRP_nM" "five_HT_nM" "KYN_nM"
## [4] "three_HK_nM" "KYNA_nM" "PIC_nM"
## [7] "Quin_nM" "AA_nM" "KYN_TRP_ratio"
## [10] "KYN_SER_ratio" "QUIN_PIC_ratio" "QUIN_KYNA_ratio"
## [13] "threeHK_KYN_ratio" "threeHK_KYNA_ratio" "IL1B_pg_mL"
## [16] "IL1B_pg_mL_LLOD" "IL2_pg_mL" "IL2_pg_mL_LLOD"
## [19] "IL4_pg_mL" "IL4_pg_mL_LLOD" "IL6_pg_mL"
## [22] "IL8_pg_mL" "IL10_pg_mL" "IL12p70_pg_mL"
## [25] "IL12p70_pg_mL_LLOD" "IL13_pg_mL" "IL13_pg_mL_LLOD"
## [28] "TNFa_pg_mL" "IFNy_pg_mL" "CRP_ng_mL"
## [31] "NIC_nM" "NIC_nM_LLOD" "NTA_nM"
## [34] "SAA_ng_mL" "VCAM_1_ng_mL" "ICAM_1_ng_mL"23 MODEL 1: IL6_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL6_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL6_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033451 1 1.016588
## age 1.092215 1 1.045091
## BMI 1.078284 1 1.038405
## Remission 1.011799 1 1.005882
## infusionno 1.000454 2 1.000113car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 81 111
## 79 109pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.00874 0.0853 Inf -0.102 0.9185
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.13420 0.0857 Inf 1.566 0.1174
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.04613 0.0854 Inf 0.540 0.5891
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.14032 0.0450 Inf -3.119 0.0052
## BL - 3rd 0.01148 0.0443 Inf 0.259 0.9638
## 1st - 3rd 0.15180 0.0444 Inf 3.416 0.0018
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.00261 0.0332 Inf 0.079 0.9966
## BL - 3rd 0.06634 0.0332 Inf 2.001 0.1120
## 1st - 3rd 0.06373 0.0329 Inf 1.935 0.1289
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="IL6_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="IL6_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
24 MODEL 1: IL8_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL8_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL8_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032307 1 1.016025
## age 1.089818 1 1.043944
## BMI 1.076409 1 1.037501
## Remission 1.011493 1 1.005730
## infusionno 1.000667 2 1.000167car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 22 45
## 21 44pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.444 0.333 Inf -1.334 0.1822
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.467 0.335 Inf -1.393 0.1636
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.164 0.333 Inf 0.493 0.6223
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1392 0.222 Inf 0.627 0.8054
## BL - 3rd 0.1905 0.219 Inf 0.870 0.6593
## 1st - 3rd 0.0513 0.220 Inf 0.234 0.9703
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1170 0.164 Inf 0.714 0.7554
## BL - 3rd 0.7990 0.164 Inf 4.876 <.0001
## 1st - 3rd 0.6820 0.163 Inf 4.187 0.0001
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="IL8_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="IL8_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
25 MODEL 1: IL10_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL10_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL10_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033936 1 1.016827
## age 1.092321 1 1.045142
## BMI 1.077451 1 1.038003
## Remission 1.011846 1 1.005906
## infusionno 1.000031 2 1.000008car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 46 173
## 45 170pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.00176 0.0295 Inf 0.060 0.9525
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.01060 0.0296 Inf 0.359 0.7199
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.01978 0.0295 Inf 0.670 0.5026
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.010568 0.01194 Inf -0.885 0.6499
## BL - 3rd -0.019354 0.01177 Inf -1.644 0.2271
## 1st - 3rd -0.008786 0.01178 Inf -0.746 0.7363
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.001723 0.00880 Inf -0.196 0.9791
## BL - 3rd -0.001332 0.00880 Inf -0.151 0.9874
## 1st - 3rd 0.000391 0.00873 Inf 0.045 0.9989
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates26 MODEL 1: IL12p70_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL12p70_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL12p70_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033717 1 1.016719
## age 1.091970 1 1.044974
## BMI 1.077160 1 1.037863
## Remission 1.011771 1 1.005868
## infusionno 1.000133 2 1.000033car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 134 63pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0302 0.0345 Inf 0.874 0.3821
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0619 0.0347 Inf 1.784 0.0744
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0388 0.0345 Inf 1.123 0.2616
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.01738 0.0198 Inf -0.878 0.6540
## BL - 3rd -0.00539 0.0195 Inf -0.276 0.9588
## 1st - 3rd 0.01199 0.0195 Inf 0.613 0.8128
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01441 0.0147 Inf 0.980 0.5894
## BL - 3rd 0.00322 0.0146 Inf 0.221 0.9735
## 1st - 3rd -0.01119 0.0146 Inf -0.766 0.7238
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates27 MODEL 1: IL12p70_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL12p70_pg_mL_LLOD~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL12p70_pg_mL_LLOD~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033664 1 1.016693
## age 1.091779 1 1.044883
## BMI 1.077143 1 1.037855
## Remission 1.011808 1 1.005887
## infusionno 1.000131 2 1.000033car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 135 63pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0301 0.0347 Inf 0.867 0.3859
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0655 0.0349 Inf 1.877 0.0606
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0389 0.0347 Inf 1.119 0.2630
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.01738 0.0200 Inf -0.868 0.6608
## BL - 3rd -0.00547 0.0197 Inf -0.277 0.9586
## 1st - 3rd 0.01191 0.0198 Inf 0.602 0.8192
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.01799 0.0148 Inf 1.218 0.4425
## BL - 3rd 0.00332 0.0148 Inf 0.225 0.9725
## 1st - 3rd -0.01467 0.0147 Inf -1.000 0.5770
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates28 MODEL 1: IL13_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IL13_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL13_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033905 1 1.016811
## age 1.092131 1 1.045051
## BMI 1.077214 1 1.037889
## Remission 1.011837 1 1.005901
## infusionno 1.000011 2 1.000003car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 64
## 133 62pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2423 0.178 Inf -1.362 0.1732
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0974 0.179 Inf -0.544 0.5864
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.1434 0.178 Inf -0.807 0.4198
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0925 0.0961 Inf -0.963 0.6005
## BL - 3rd -0.0612 0.0947 Inf -0.646 0.7946
## 1st - 3rd 0.0313 0.0949 Inf 0.330 0.9418
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0524 0.0726 Inf 0.722 0.7503
## BL - 3rd 0.0377 0.0714 Inf 0.527 0.8580
## 1st - 3rd -0.0148 0.0715 Inf -0.207 0.9767
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates29 MODEL 1: IL13_pg_mL_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(IL13_pg_mL_LLOD~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IL13_pg_mL_LLOD~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033899 1 1.016808
## age 1.092125 1 1.045048
## BMI 1.077225 1 1.037895
## Remission 1.011841 1 1.005903
## infusionno 1.000011 2 1.000003car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 137 101
## 135 99pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.227 0.180 Inf -1.261 0.2072
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.061 0.181 Inf -0.337 0.7362
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.146 0.180 Inf -0.808 0.4193
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0930 0.1009 Inf -0.922 0.6264
## BL - 3rd -0.0611 0.0995 Inf -0.614 0.8125
## 1st - 3rd 0.0320 0.0997 Inf 0.321 0.9449
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.0734 0.0744 Inf 0.987 0.5849
## BL - 3rd 0.0207 0.0744 Inf 0.278 0.9582
## 1st - 3rd -0.0527 0.0739 Inf -0.714 0.7553
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates30 MODEL 1: TNFa_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(TNFa_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(TNFa_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033867 1 1.016793
## age 1.092316 1 1.045139
## BMI 1.077576 1 1.038064
## Remission 1.011837 1 1.005901
## infusionno 1.000095 2 1.000024car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 101 212
## 99 209pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.01137 0.0815 Inf -0.139 0.8891
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.01936 0.0817 Inf -0.237 0.8127
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.00708 0.0816 Inf -0.087 0.9309
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.044055 0.0277 Inf 1.589 0.2503
## BL - 3rd 0.030777 0.0273 Inf 1.127 0.4976
## 1st - 3rd -0.013277 0.0273 Inf -0.486 0.8781
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.036069 0.0204 Inf 1.767 0.1808
## BL - 3rd 0.035072 0.0204 Inf 1.718 0.1984
## 1st - 3rd -0.000997 0.0203 Inf -0.049 0.9987
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="TNFa_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="TNFa_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
31 MODEL 1: IFNy_pg_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(IFNy_pg_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(IFNy_pg_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032253 1 1.015998
## age 1.089756 1 1.043914
## BMI 1.076370 1 1.037483
## Remission 1.011471 1 1.005719
## infusionno 1.000680 2 1.000170car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 167 173
## 164 170pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0141 0.679 Inf 0.021 0.9834
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.1596 0.680 Inf -0.235 0.8145
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.4022 0.679 Inf -0.592 0.5538
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.371 0.239 Inf 1.551 0.2674
## BL - 3rd 0.209 0.236 Inf 0.887 0.6485
## 1st - 3rd -0.162 0.236 Inf -0.686 0.7717
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.197 0.176 Inf 1.120 0.5017
## BL - 3rd -0.207 0.176 Inf -1.175 0.4680
## 1st - 3rd -0.404 0.175 Inf -2.313 0.0541
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="IFNy_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="IFNy_pg_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
32 MODEL 1: CRP_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(CRP_ng_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(CRP_ng_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032732 1 1.016234
## age 1.089969 1 1.044016
## BMI 1.075499 1 1.037063
## Remission 1.011504 1 1.005736
## infusionno 1.000227 2 1.000057car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 210 155
## 207 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 668 480 Inf 1.393 0.1635
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 638 480 Inf 1.330 0.1837
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 522 480 Inf 1.088 0.2766
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 119.5 107.7 Inf 1.109 0.5082
## BL - 3rd 284.4 106.1 Inf 2.680 0.0201
## 1st - 3rd 164.9 106.2 Inf 1.553 0.2662
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 89.4 79.3 Inf 1.127 0.4971
## BL - 3rd 138.0 79.3 Inf 1.740 0.1903
## 1st - 3rd 48.6 78.6 Inf 0.618 0.8105
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="CRP_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="CRP_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
33 MODEL 1: NIC_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(NIC_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NIC_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033905 1 1.016811
## age 1.092319 1 1.045141
## BMI 1.077508 1 1.038031
## Remission 1.011842 1 1.005904
## infusionno 1.000060 2 1.000015car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 10 155
## 9 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0688 0.142 Inf -0.484 0.6284
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2631 0.143 Inf -1.839 0.0659
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0352 0.142 Inf 0.247 0.8050
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1009 0.0862 Inf 1.171 0.4707
## BL - 3rd -0.0286 0.0850 Inf -0.336 0.9395
## 1st - 3rd -0.1295 0.0852 Inf -1.520 0.2815
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0934 0.0636 Inf -1.468 0.3062
## BL - 3rd 0.0754 0.0636 Inf 1.186 0.4617
## 1st - 3rd 0.1687 0.0632 Inf 2.671 0.0207
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="NIC_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="NIC_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
34 MODEL 1: NIC_nM_LLOD by Remission*infusionno
rlmer<-robustlmm::rlmer(NIC_nM_LLOD~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NIC_nM_LLOD~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033905 1 1.016811
## age 1.092319 1 1.045141
## BMI 1.077508 1 1.038031
## Remission 1.011842 1 1.005904
## infusionno 1.000060 2 1.000015car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 10 155
## 9 152pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.0708 0.142 Inf -0.499 0.6176
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -0.2628 0.143 Inf -1.842 0.0654
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.0353 0.142 Inf 0.249 0.8037
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.1009 0.0858 Inf 1.176 0.4677
## BL - 3rd -0.0286 0.0846 Inf -0.338 0.9389
## 1st - 3rd -0.1295 0.0848 Inf -1.527 0.2784
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0911 0.0633 Inf -1.439 0.3207
## BL - 3rd 0.0775 0.0633 Inf 1.224 0.4389
## 1st - 3rd 0.1686 0.0629 Inf 2.681 0.0201
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="NIC_nM_LLOD (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="NIC_nM_LLOD (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
35 MODEL 1: NTA_nM by Remission*infusionno
rlmer<-robustlmm::rlmer(NTA_nM~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(NTA_nM~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032912 1 1.016323
## age 1.091852 1 1.044917
## BMI 1.079024 1 1.038761
## Remission 1.011785 1 1.005875
## infusionno 1.000851 2 1.000213car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 22 32
## 21 31pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -33.18 34.4 Inf -0.965 0.3343
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -64.81 34.6 Inf -1.872 0.0613
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1.99 34.4 Inf 0.058 0.9539
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 43.7 25.4 Inf 1.722 0.1970
## BL - 3rd 14.8 25.0 Inf 0.592 0.8246
## 1st - 3rd -28.9 25.1 Inf -1.149 0.4837
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 12.0 18.7 Inf 0.643 0.7961
## BL - 3rd 50.0 18.7 Inf 2.668 0.0208
## 1st - 3rd 37.9 18.6 Inf 2.036 0.1037
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="NTA_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="NTA_nM (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
36 MODEL 1: SAA_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(SAA_ng_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(SAA_ng_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.031928 1 1.015839
## age 1.089416 1 1.043751
## BMI 1.075200 1 1.036918
## Remission 1.011144 1 1.005556
## infusionno 1.000411 2 1.000103car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 148 163
## 145 160pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 481 426 Inf 1.130 0.2585
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 442 427 Inf 1.037 0.2999
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 427 426 Inf 1.003 0.3160
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 100.551 132.4 Inf 0.759 0.7280
## BL - 3rd 115.401 130.5 Inf 0.884 0.6502
## 1st - 3rd 14.850 130.6 Inf 0.114 0.9929
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 61.429 97.5 Inf 0.630 0.8036
## BL - 3rd 61.274 97.5 Inf 0.628 0.8045
## 1st - 3rd -0.155 96.7 Inf -0.002 1.0000
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="SAA_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="SAA_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
37 MODEL 1: VCAM_1_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(VCAM_1_ng_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(VCAM_1_ng_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.033005 1 1.016368
## age 1.090600 1 1.044318
## BMI 1.076458 1 1.037525
## Remission 1.011669 1 1.005818
## infusionno 1.000349 2 1.000087car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 53 212
## 52 209pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 1.24 16.7 Inf 0.074 0.9411
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 10.71 16.8 Inf 0.638 0.5234
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 14.26 16.7 Inf 0.852 0.3944
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.694 7.24 Inf -0.096 0.9949
## BL - 3rd 2.951 7.13 Inf 0.414 0.9100
## 1st - 3rd 3.645 7.14 Inf 0.510 0.8662
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 8.782 5.33 Inf 1.648 0.2258
## BL - 3rd 15.977 5.33 Inf 2.997 0.0077
## 1st - 3rd 7.195 5.29 Inf 1.360 0.3622
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="VCAM_1_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="VCAM_1_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
38 MODEL 1: ICAM_1_ng_mL by Remission*infusionno
rlmer<-robustlmm::rlmer(ICAM_1_ng_mL~sex+age+BMI+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
rlmer_vif<-lme4::lmer(ICAM_1_ng_mL~sex+age+BMI+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(rlmer_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.032232 1 1.015988
## age 1.089540 1 1.043810
## BMI 1.075097 1 1.036869
## Remission 1.011261 1 1.005615
## infusionno 1.000286 2 1.000071car::qqPlot(residuals(rlmer), main="QQ-PLOT")
## 17 15
## 16 14pairwise_remission<-emmeans(rlmer, pairwise~Remission|infusionno)
pairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 23.5 17.7 Inf 1.330 0.1836
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 22.1 17.8 Inf 1.245 0.2132
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 29.6 17.7 Inf 1.669 0.0950
##
## Results are averaged over the levels of: sexpairwise_remission<-emmeans(rlmer, pairwise~infusionno|Remission)
pairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 4.48 7.27 Inf 0.616 0.8115
## BL - 3rd 7.45 7.17 Inf 1.040 0.5515
## 1st - 3rd 2.98 7.17 Inf 0.415 0.9095
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st 3.02 5.36 Inf 0.565 0.8389
## BL - 3rd 13.48 5.36 Inf 2.516 0.0318
## 1st - 3rd 10.45 5.32 Inf 1.966 0.1206
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatespairwise_infusionno<-emmeans(rlmer, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="ICAM_1_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
pairwise_infusionno<-emmeans(rlmer, pairwise~Remission|infusionno)
plot(pairwise_infusionno, xlab="ICAM_1_ng_mL (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
# 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 | |||
39 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 | |||
40 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 | |||
41 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 | |||
42 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 | |||
43 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 | |||
44 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 | |||
45 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 | |||
46 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 | |||
47 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 | |||
48 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 | |||
49 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 | |||
50 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 | |||
51 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 | |||
52 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 | |||
53 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 | |||
54 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 | |||
55 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 | |||
56 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 | |||
57 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 | |||
58 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 | |||
59 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 | |||
60 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 | |||
61 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 | |||
62 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 | |||
63 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 | |||
64 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 | |||
65 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 | |||
66 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 | |||
67 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 | |||
68 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 | |||
69 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 | |||
70 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 | |||
71 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 | |||
72 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 | |||
73 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 | |||
74 MODEL 3: MADRS_Score_3rd by TRP_nM_BL
Biok_wide_new$TRP_nM_BL<-exp(Biok_wide_new$TRP_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+TRP_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 4 25
## 2 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by TRP_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.27 | -0.67 – 0.12 | 0.090 |
| age | 0.08 | -0.17 – 0.32 | 0.536 |
| sex [female] | 0.43 | -0.08 – 0.93 | 0.095 |
| BMI | 0.04 | -0.20 – 0.28 | 0.727 |
| MADRS Score BL | 0.23 | -0.00 – 0.47 | 0.052 |
| TRP nM BL | 0.27 | 0.03 – 0.51 | 0.030 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.152 / 0.086 | ||
75 MODEL 3: MADRS_Score_3rd by five_HT_nM_BL
Biok_wide_new$five_HT_nM_BL<-exp(Biok_wide_new$five_HT_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+five_HT_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by five_HT_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.13 | -0.52 – 0.25 | 0.262 |
| age | 0.10 | -0.14 – 0.35 | 0.404 |
| sex [female] | 0.21 | -0.27 – 0.69 | 0.390 |
| BMI | 0.04 | -0.20 – 0.28 | 0.718 |
| MADRS Score BL | 0.28 | 0.05 – 0.52 | 0.020 |
| five HT nM BL | 0.28 | 0.05 – 0.52 | 0.020 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.161 / 0.095 | ||
76 MODEL 3: MADRS_Score_3rd by KYN_nM_BL
Biok_wide_new$KYN_nM_BL<-exp(Biok_wide_new$KYN_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.23 | -0.63 – 0.17 | 0.356 |
| age | -0.01 | -0.28 – 0.25 | 0.916 |
| sex [female] | 0.36 | -0.15 – 0.86 | 0.165 |
| BMI | -0.01 | -0.25 – 0.24 | 0.965 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.040 |
| KYN nM BL | 0.19 | -0.08 – 0.45 | 0.163 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.114 / 0.045 | ||
77 MODEL 3: MADRS_Score_3rd by three_HK_nM_BL
Biok_wide_new$three_HK_nM_BL<-exp(Biok_wide_new$three_HK_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+three_HK_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by three_HK_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.625 |
| age | 0.05 | -0.21 – 0.30 | 0.702 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.272 |
| BMI | 0.01 | -0.24 – 0.27 | 0.912 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.043 |
| three HK nM BL | 0.02 | -0.23 – 0.26 | 0.895 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.015 | ||
78 MODEL 3: MADRS_Score_3rd by KYNA_nM_BL
Biok_wide_new$KYNA_nM_BL<-exp(Biok_wide_new$KYNA_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYNA_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYNA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.58 – 0.24 | 0.697 |
| age | 0.05 | -0.20 – 0.31 | 0.682 |
| sex [female] | 0.27 | -0.25 – 0.79 | 0.303 |
| BMI | 0.02 | -0.23 – 0.27 | 0.893 |
| MADRS Score BL | 0.25 | 0.00 – 0.50 | 0.050 |
| KYNA nM BL | -0.02 | -0.28 – 0.25 | 0.907 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.015 | ||
79 MODEL 3: MADRS_Score_3rd by PIC_nM_BL
Biok_wide_new$PIC_nM_BL<-exp(Biok_wide_new$PIC_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+PIC_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by PIC_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.697 |
| age | 0.06 | -0.20 – 0.31 | 0.656 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.268 |
| BMI | 0.01 | -0.24 – 0.26 | 0.922 |
| MADRS Score BL | 0.25 | 0.00 – 0.49 | 0.047 |
| PIC nM BL | -0.05 | -0.29 – 0.19 | 0.687 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.089 / 0.018 | ||
80 MODEL 3: MADRS_Score_3rd by Quin_nM_BL
Biok_wide_new$Quin_nM_BL<-exp(Biok_wide_new$Quin_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+Quin_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by Quin_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.12 | -0.53 – 0.29 | 0.904 |
| age | 0.08 | -0.17 – 0.34 | 0.520 |
| sex [female] | 0.19 | -0.33 – 0.71 | 0.465 |
| BMI | 0.03 | -0.22 – 0.28 | 0.802 |
| MADRS Score BL | 0.26 | 0.02 – 0.50 | 0.037 |
| Quin nM BL | -0.14 | -0.40 – 0.12 | 0.282 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.103 / 0.033 | ||
81 MODEL 3: MADRS_Score_3rd by AA_nM_BL
Biok_wide_new$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)
summary(lm_TRP_BL)##
## Call:
## lm(formula = MADRS_Score_3rd ~ age + sex + BMI + MADRS_Score_BL +
## AA_nM_BL, data = Biok_wide_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.439 -5.453 -1.580 4.026 16.156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.70742 6.54162 -0.414 0.6803
## age 0.05199 0.06785 0.766 0.4464
## sexfemale 2.24468 1.73579 1.293 0.2006
## BMI 0.12135 0.15840 0.766 0.4464
## MADRS_Score_BL 0.26030 0.14916 1.745 0.0858 .
## AA_nM_BL -0.49032 0.22659 -2.164 0.0342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.854 on 64 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1488, Adjusted R-squared: 0.08231
## F-statistic: 2.238 on 5 and 64 DF, p-value: 0.06112lm_TRP_BL<-lm(MADRS_Score_3rd~MADRS_Score_BL+AA_nM_BL, data=Biok_wide_new)
summary(lm_TRP_BL)##
## Call:
## lm(formula = MADRS_Score_3rd ~ MADRS_Score_BL + AA_nM_BL, data = Biok_wide_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.360 -5.386 -1.701 3.661 17.529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5313 4.5776 0.771 0.4432
## MADRS_Score_BL 0.2712 0.1459 1.859 0.0674 .
## AA_nM_BL -0.3832 0.2100 -1.825 0.0724 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.845 on 67 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1113, Adjusted R-squared: 0.08482
## F-statistic: 4.198 on 2 and 67 DF, p-value: 0.01916car::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 AA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | 0.00 | -0.23 – 0.23 | 0.443 |
| MADRS Score BL | 0.22 | -0.02 – 0.45 | 0.067 |
| AA nM BL | -0.21 | -0.45 – 0.02 | 0.072 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.111 / 0.085 | ||
82 MODEL 3: MADRS_Score_3rd by KYN_TRP_ratio_BL
Biok_wide_new$KYN_TRP_ratio_BL<-exp(Biok_wide_new$KYN_TRP_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_TRP_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_TRP_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.58 – 0.22 | 0.683 |
| age | 0.06 | -0.21 – 0.33 | 0.670 |
| sex [female] | 0.28 | -0.22 – 0.78 | 0.271 |
| BMI | 0.02 | -0.23 – 0.28 | 0.867 |
| MADRS Score BL | 0.25 | 0.00 – 0.50 | 0.046 |
| KYN TRP ratio BL | -0.02 | -0.30 – 0.25 | 0.872 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.087 / 0.016 | ||
83 MODEL 3: MADRS_Score_3rd by KYN_SER_ratio_BL
Biok_wide_new$KYN_SER_ratio_BL<-exp(Biok_wide_new$KYN_SER_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_SER_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by KYN_SER_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.20 | 0.477 |
| age | 0.07 | -0.18 – 0.32 | 0.574 |
| sex [female] | 0.31 | -0.19 – 0.81 | 0.221 |
| BMI | 0.02 | -0.23 – 0.26 | 0.895 |
| MADRS Score BL | 0.30 | 0.04 – 0.55 | 0.022 |
| KYN SER ratio BL | -0.15 | -0.40 – 0.10 | 0.239 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.106 / 0.036 | ||
84 MODEL 3: MADRS_Score_3rd by QUIN_PIC_ratio_BL
Biok_wide_new$QUIN_PIC_ratio_BL<-exp(Biok_wide_new$QUIN_PIC_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+QUIN_PIC_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by QUIN_PIC_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.59 – 0.20 | 0.579 |
| age | 0.07 | -0.19 – 0.32 | 0.590 |
| sex [female] | 0.30 | -0.19 – 0.80 | 0.228 |
| BMI | 0.05 | -0.21 – 0.30 | 0.709 |
| MADRS Score BL | 0.28 | 0.03 – 0.52 | 0.029 |
| QUIN PIC ratio BL | -0.13 | -0.38 – 0.12 | 0.300 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.102 / 0.032 | ||
85 MODEL 3: MADRS_Score_3rd by QUIN_KYNA_ratio_BL
Biok_wide_new$QUIN_KYNA_ratio_BL<-exp(Biok_wide_new$QUIN_KYNA_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+QUIN_KYNA_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by QUIN_KYNA_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.660 |
| age | 0.05 | -0.20 – 0.31 | 0.680 |
| sex [female] | 0.30 | -0.20 – 0.80 | 0.240 |
| BMI | 0.03 | -0.22 – 0.27 | 0.839 |
| MADRS Score BL | 0.27 | 0.02 – 0.53 | 0.034 |
| QUIN KYNA ratio BL | -0.08 | -0.33 – 0.17 | 0.536 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.092 / 0.021 | ||
86 MODEL 3: MADRS_Score_3rd by threeHK_KYN_ratio_BL
Biok_wide_new$threeHK_KYN_ratio_BL<-exp(Biok_wide_new$threeHK_KYN_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+threeHK_KYN_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by threeHK_KYN_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.20 | 0.934 |
| age | 0.03 | -0.22 – 0.29 | 0.788 |
| sex [female] | 0.31 | -0.19 – 0.81 | 0.226 |
| BMI | 0.04 | -0.21 – 0.29 | 0.776 |
| MADRS Score BL | 0.23 | -0.01 – 0.48 | 0.062 |
| threeHK KYN ratio BL | -0.11 | -0.36 – 0.13 | 0.360 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
87 MODEL 3: MADRS_Score_3rd by threeHK_KYNA_ratio_BL
Biok_wide_new$threeHK_KYNA_ratio_BL<-exp(Biok_wide_new$threeHK_KYNA_ratio_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+threeHK_KYNA_ratio_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 25
## 1 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by threeHK_KYNA_ratio_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.14 | -0.54 – 0.27 | 0.629 |
| age | 0.05 | -0.20 – 0.30 | 0.680 |
| sex [female] | 0.21 | -0.31 – 0.73 | 0.416 |
| BMI | -0.01 | -0.26 – 0.24 | 0.943 |
| MADRS Score BL | 0.24 | -0.00 – 0.49 | 0.051 |
| threeHK KYNA ratio BL | 0.12 | -0.14 – 0.37 | 0.362 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
88 MODEL 3: MADRS_Score_3rd by IL1B_pg_mL_BL
Biok_wide_new$IL1B_pg_mL_BL<-exp(Biok_wide_new$IL1B_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL1B_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 19 63
## 2 13sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL1B_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | 0.42 | -0.49 – 1.33 | 0.809 |
| age | -0.21 | -0.94 – 0.53 | 0.548 |
| sex [female] | -0.83 | -2.30 – 0.64 | 0.235 |
| BMI | -0.04 | -0.77 – 0.69 | 0.898 |
| MADRS Score BL | 0.50 | -0.44 – 1.43 | 0.263 |
| IL1B pg mL BL | 0.43 | -0.20 – 1.06 | 0.158 |
| Observations | 16 | ||
| R2 / R2 adjusted | 0.386 / 0.079 | ||
89 MODEL 3: MADRS_Score_3rd by IL1B_pg_mL_LLOD_BL
Biok_wide_new$IL1B_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL1B_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL1B_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL1B_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.568 |
| age | 0.05 | -0.20 – 0.30 | 0.692 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.292 |
| BMI | 0.02 | -0.23 – 0.27 | 0.880 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL1B pg mL LLOD BL | 0.05 | -0.19 – 0.29 | 0.698 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.089 / 0.018 | ||
90 MODEL 3: MADRS_Score_3rd by IL2_pg_mL_BL
Biok_wide_new$IL2_pg_mL_BL<-exp(Biok_wide_new$IL2_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL2_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 22
## 1 13sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL2_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | 0.14 | -0.37 – 0.65 | 0.895 |
| age | 0.19 | -0.13 – 0.51 | 0.242 |
| sex [female] | -0.21 | -0.85 – 0.42 | 0.499 |
| BMI | -0.10 | -0.41 – 0.21 | 0.505 |
| MADRS Score BL | 0.33 | 0.02 – 0.64 | 0.036 |
| IL2 pg mL BL | 0.27 | -0.04 – 0.57 | 0.087 |
| Observations | 44 | ||
| R2 / R2 adjusted | 0.195 / 0.089 | ||
91 MODEL 3: MADRS_Score_3rd by IL2_pg_mL_LLOD_BL
Biok_wide_new$IL2_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL2_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL2_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL2_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.56 – 0.23 | 0.593 |
| age | 0.07 | -0.19 – 0.32 | 0.603 |
| sex [female] | 0.26 | -0.24 – 0.75 | 0.305 |
| BMI | 0.03 | -0.22 – 0.27 | 0.819 |
| MADRS Score BL | 0.24 | -0.00 – 0.48 | 0.054 |
| IL2 pg mL LLOD BL | 0.16 | -0.09 – 0.40 | 0.201 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.110 / 0.040 | ||
92 MODEL 3: MADRS_Score_3rd by IL4_pg_mL_BL
Biok_wide_new$IL4_pg_mL_BL<-exp(Biok_wide_new$IL4_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL4_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL4_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.515 |
| age | 0.07 | -0.19 – 0.33 | 0.606 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.293 |
| BMI | 0.00 | -0.25 – 0.26 | 0.969 |
| MADRS Score BL | 0.27 | 0.02 – 0.52 | 0.037 |
| IL4 pg mL BL | 0.06 | -0.19 – 0.32 | 0.622 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
93 MODEL 3: MADRS_Score_3rd by IL4_pg_mL_LLOD_BL
Biok_wide_new$IL4_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL4_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL4_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL4_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.515 |
| age | 0.07 | -0.19 – 0.33 | 0.606 |
| sex [female] | 0.27 | -0.24 – 0.77 | 0.293 |
| BMI | 0.00 | -0.25 – 0.26 | 0.969 |
| MADRS Score BL | 0.27 | 0.02 – 0.52 | 0.037 |
| IL4 pg mL LLOD BL | 0.06 | -0.19 – 0.32 | 0.622 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
94 MODEL 3: MADRS_Score_3rd by IL6_pg_mL_BL
Biok_wide_new$IL6_pg_mL_BL<-exp(Biok_wide_new$IL6_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL6_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL6_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.61 – 0.20 | 0.553 |
| age | 0.06 | -0.19 – 0.32 | 0.624 |
| sex [female] | 0.31 | -0.20 – 0.83 | 0.226 |
| BMI | 0.05 | -0.22 – 0.32 | 0.716 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.036 |
| IL6 pg mL BL | -0.08 | -0.36 – 0.19 | 0.539 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.092 / 0.021 | ||
95 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
96 MODEL 3: MADRS_Score_3rd by IL10_pg_mL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
97 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
98 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
99 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
100 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
101 MODEL 3: MADRS_Score_3rd by IL8_pg_mL_BL
Biok_wide_new$IL8_pg_mL_BL<-exp(Biok_wide_new$IL8_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL8_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL8_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.18 | -0.59 – 0.22 | 0.593 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.29 | -0.22 – 0.79 | 0.261 |
| BMI | 0.02 | -0.23 – 0.27 | 0.885 |
| MADRS Score BL | 0.26 | 0.01 – 0.50 | 0.041 |
| IL8 pg mL BL | 0.03 | -0.21 – 0.28 | 0.782 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
102 MODEL 3: MADRS_Score_3rd by IL10_pg_mL_BL
Biok_wide_new$IL10_pg_mL_BL<-exp(Biok_wide_new$IL10_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL10_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL10_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.21 | -0.60 – 0.19 | 0.634 |
| age | 0.07 | -0.18 – 0.32 | 0.590 |
| sex [female] | 0.32 | -0.17 – 0.82 | 0.200 |
| BMI | 0.01 | -0.23 – 0.26 | 0.914 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.043 |
| IL10 pg mL BL | -0.18 | -0.42 – 0.06 | 0.137 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.118 / 0.049 | ||
103 MODEL 3: MADRS_Score_3rd by IL12p70_pg_mL_BL
Biok_wide_new$IL12p70_pg_mL_BL<-exp(Biok_wide_new$IL12p70_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL12p70_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL12p70_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.599 |
| age | 0.07 | -0.19 – 0.33 | 0.612 |
| sex [female] | 0.30 | -0.21 – 0.81 | 0.243 |
| BMI | 0.00 | -0.25 – 0.26 | 0.972 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.038 |
| IL12p70 pg mL BL | 0.07 | -0.19 – 0.32 | 0.604 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
104 MODEL 3: MADRS_Score_3rd by IL12p70_pg_mL_LLOD_BL
Biok_wide_new$IL12p70_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL12p70_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL12p70_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL12p70_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.599 |
| age | 0.07 | -0.19 – 0.33 | 0.612 |
| sex [female] | 0.30 | -0.21 – 0.81 | 0.243 |
| BMI | 0.00 | -0.25 – 0.26 | 0.972 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.038 |
| IL12p70 pg mL LLOD BL | 0.07 | -0.19 – 0.32 | 0.604 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
105 MODEL 3: MADRS_Score_3rd by IL13_pg_mL_BL
Biok_wide_new$IL13_pg_mL_BL<-exp(Biok_wide_new$IL13_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL13_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL13_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.59 – 0.24 | 0.645 |
| age | 0.06 | -0.20 – 0.33 | 0.627 |
| sex [female] | 0.27 | -0.25 – 0.78 | 0.304 |
| BMI | 0.00 | -0.25 – 0.26 | 0.978 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.044 |
| IL13 pg mL BL | 0.06 | -0.20 – 0.31 | 0.650 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.082 / 0.009 | ||
106 MODEL 3: MADRS_Score_3rd by IL13_pg_mL_LLOD_BL
Biok_wide_new$IL13_pg_mL_LLOD_BL<-exp(Biok_wide_new$IL13_pg_mL_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IL13_pg_mL_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IL13_pg_mL_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.21 | 0.585 |
| age | 0.07 | -0.19 – 0.33 | 0.607 |
| sex [female] | 0.30 | -0.21 – 0.80 | 0.244 |
| BMI | 0.00 | -0.25 – 0.26 | 0.976 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| IL13 pg mL LLOD BL | 0.07 | -0.19 – 0.32 | 0.605 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
107 MODEL 3: MADRS_Score_3rd by TNFa_pg_mL_BL
Biok_wide_new$TNFa_pg_mL_BL<-exp(Biok_wide_new$TNFa_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+TNFa_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by TNFa_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.59 – 0.22 | 0.588 |
| age | 0.05 | -0.21 – 0.30 | 0.714 |
| sex [female] | 0.29 | -0.22 – 0.81 | 0.259 |
| BMI | 0.02 | -0.23 – 0.26 | 0.898 |
| MADRS Score BL | 0.26 | 0.01 – 0.51 | 0.042 |
| TNFa pg mL BL | 0.03 | -0.22 – 0.29 | 0.793 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.016 | ||
108 MODEL 3: MADRS_Score_3rd by IFNy_pg_mL_BL
Biok_wide_new$IFNy_pg_mL_BL<-exp(Biok_wide_new$IFNy_pg_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+IFNy_pg_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by IFNy_pg_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.699 |
| age | 0.06 | -0.20 – 0.31 | 0.657 |
| sex [female] | 0.27 | -0.23 – 0.76 | 0.292 |
| BMI | -0.00 | -0.25 – 0.25 | 0.997 |
| MADRS Score BL | 0.28 | 0.03 – 0.53 | 0.029 |
| IFNy pg mL BL | -0.11 | -0.36 – 0.13 | 0.368 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.098 / 0.028 | ||
109 MODEL 3: MADRS_Score_3rd by CRP_ng_mL_BL
Biok_wide_new$CRP_ng_mL_BL<-exp(Biok_wide_new$CRP_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+CRP_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by CRP_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.16 | -0.56 – 0.24 | 0.797 |
| age | 0.06 | -0.20 – 0.31 | 0.660 |
| sex [female] | 0.25 | -0.25 – 0.75 | 0.321 |
| BMI | -0.03 | -0.30 – 0.23 | 0.805 |
| MADRS Score BL | 0.25 | 0.01 – 0.49 | 0.045 |
| CRP ng mL BL | 0.13 | -0.13 – 0.38 | 0.335 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.100 / 0.029 | ||
110 MODEL 3: MADRS_Score_3rd by NIC_nM_BL
Biok_wide_new$NIC_nM_BL<-exp(Biok_wide_new$NIC_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NIC_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NIC_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.619 |
| age | 0.05 | -0.20 – 0.30 | 0.685 |
| sex [female] | 0.26 | -0.24 – 0.76 | 0.301 |
| BMI | 0.01 | -0.23 – 0.26 | 0.915 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| NIC nM BL | 0.07 | -0.17 – 0.31 | 0.568 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.091 / 0.020 | ||
111 MODEL 3: MADRS_Score_3rd by NIC_nM_LLOD_BL
Biok_wide_new$NIC_nM_LLOD_BL<-exp(Biok_wide_new$NIC_nM_LLOD_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NIC_nM_LLOD_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NIC_nM_LLOD_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.23 | 0.619 |
| age | 0.05 | -0.20 – 0.30 | 0.685 |
| sex [female] | 0.26 | -0.24 – 0.76 | 0.301 |
| BMI | 0.01 | -0.23 – 0.26 | 0.915 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.038 |
| NIC nM LLOD BL | 0.07 | -0.17 – 0.31 | 0.568 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.091 / 0.020 | ||
112 MODEL 3: MADRS_Score_3rd by NTA_nM_BL
Biok_wide_new$NTA_nM_BL<-exp(Biok_wide_new$NTA_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+NTA_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by NTA_nM_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.20 | -0.60 – 0.21 | 0.472 |
| age | 0.07 | -0.19 – 0.32 | 0.612 |
| sex [female] | 0.30 | -0.20 – 0.81 | 0.234 |
| BMI | 0.02 | -0.22 – 0.27 | 0.846 |
| MADRS Score BL | 0.26 | 0.02 – 0.51 | 0.037 |
| NTA nM BL | 0.08 | -0.16 – 0.33 | 0.500 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.093 / 0.022 | ||
113 MODEL 3: MADRS_Score_3rd by SAA_ng_mL_BL
Biok_wide_new$SAA_ng_mL_BL<-exp(Biok_wide_new$SAA_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+SAA_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by SAA_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.16 | -0.57 – 0.24 | 0.722 |
| age | 0.05 | -0.21 – 0.30 | 0.708 |
| sex [female] | 0.25 | -0.26 – 0.76 | 0.333 |
| BMI | -0.01 | -0.28 – 0.26 | 0.938 |
| MADRS Score BL | 0.25 | 0.00 – 0.49 | 0.048 |
| SAA ng mL BL | 0.07 | -0.20 – 0.34 | 0.616 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.090 / 0.019 | ||
114 MODEL 3: MADRS_Score_3rd by VCAM_1_ng_mL_BL
Biok_wide_new$VCAM_1_ng_mL_BL<-exp(Biok_wide_new$VCAM_1_ng_mL_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+VCAM_1_ng_mL_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Multiple linear model: MADRS_Score_3rd by VCAM_1_ng_mL_BL")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.57 – 0.24 | 0.869 |
| age | 0.04 | -0.21 – 0.30 | 0.738 |
| sex [female] | 0.26 | -0.25 – 0.77 | 0.312 |
| BMI | 0.02 | -0.23 – 0.27 | 0.882 |
| MADRS Score BL | 0.25 | -0.00 – 0.49 | 0.052 |
| VCAM 1 ng mL BL | -0.04 | -0.29 – 0.21 | 0.763 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.088 / 0.017 | ||
115 MODEL 3: MADRS_Score_3rd by ICAM_1_ng_mL_BL
Biok_wide_new$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 | ||