Biok study: updated models
Stephen Murata
2/28/2022
1 Sample characteristics by Remission
compareGroups::descrTable(Remission~.
, Biok_vert_df_raw,
hide.no = '0',
show.p.overall = FALSE,
include.label = TRUE)## Warning in compareGroups.fit(X = X, y = y, include.label = include.label, :
## Variables 'Sample_ID' have been removed since some errors occurred##
## --------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))## Warning in compareGroups.fit(X = X, y = y, include.label = include.label, :
## Variables 'Sample_ID' have been removed since some errors occurred## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero
## Warning in cor(as.integer(x), as.integer(y)): the standard deviation is zero##
## --------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()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).## Warning: Removed 1 rows containing missing values (geom_vline).
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()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_vline).
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()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 112 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_vline).
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)## Warning: Removed 4 row(s) containing missing values (geom_path).## Warning: Removed 5 rows containing missing values (geom_point).
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)## Warning: Removed 5 rows containing non-finite values (stat_boxplot).## Warning: Removed 5 rows containing non-finite values (stat_signif).## Warning: Removed 5 rows containing non-finite values (stat_compare_means).## Warning: Removed 5 rows containing missing values (geom_point).
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)## Warning: Removed 5 rows containing non-finite values (stat_boxplot).## Warning: Removed 5 rows containing non-finite values (stat_compare_means).## Warning: Removed 5 rows containing missing values (geom_point).
6 Univariate screen (whole group, 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$P,
insig="blank",
order="hclust",
method="color",
type="upper",
diag=FALSE,
number.cex=0.8,
na.label.col = "gray",
addgrid.col=TRUE,
tl.col = 'red',
tl.srt = 45,
title="Pearson's matrix of age, BMI, and log transformed biomarkers (whole sample)")
7 Model 1: Biomarker by Remission:infusionno
8 Model 1a (clmm): ordinal regressions for IL1B
#IL1B
rlmer_IL1B<-ordinal::clmm(as.factor(IL1B_pg_mL)~sex+age+BMI+Remission*infusionno+(1|patientno), data=Biok_vert_df,nAGQ=10)## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.pairwise_remission<-emmeans(rlmer_IL1B, pairwise~Remission|infusionno)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimatespairwise_remission$contrasts## infusionno = BL:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 2.14 1.79 Inf 1.196 0.2315
##
## infusionno = 1st:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter -1.69 2.23 Inf -0.757 0.4491
##
## infusionno = 3rd:
## contrast estimate SE df z.ratio p.value
## No remission - Remitter 0.98 1.70 Inf 0.575 0.5651
##
## Results are averaged over the levels of: sex
## Note: contrasts are still on the as.factor scalepairwise_remission<-emmeans(rlmer_IL1B, pairwise~infusionno|Remission)## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimatespairwise_remission$contrasts## Remission = No remission:
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.8590 2.118 Inf 0.878 0.6544
## BL - 3rd -0.0128 1.361 Inf -0.009 1.0000
## 1st - 3rd -1.8717 2.028 Inf -0.923 0.6257
##
## Remission = Remitter:
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.9737 1.121 Inf -1.761 0.1830
## BL - 3rd -1.1767 0.941 Inf -1.251 0.4234
## 1st - 3rd 0.7970 1.106 Inf 0.721 0.7513
##
## Results are averaged over the levels of: sex
## Note: contrasts are still on the as.factor scale
## P value adjustment: tukey method for comparing a family of 3 estimates9 Model 1: ordinal regressions for IL2
Note: error for IL2 model “cannot compute vcov: Hessian is not positive”
10 Model 2: Depression by biomarker:infusionno
Varnames_bx<-Biok_vert_df %>%
dplyr::select(-all_of(vars_demo),
-all_of(vars_tx),
-all_of(vars_cx),
vars_KP,
vars_inflam,
vars_vasc) %>% names()## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(vars_KP)` instead of `vars_KP` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(vars_inflam)` instead of `vars_inflam` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(vars_vasc)` instead of `vars_vasc` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.model2<-list()
model2_nobmi<-list()
emtrends_mod2<-list()
emtrend_contrasts<-list()
emtrend_confint<-list()
for (x in Varnames_bx){
mod2<-rlmer(substitute(MADRS_Score~age+sex+BMI+i*infusionno+(1|patientno)+(1|Site_Location), list(i=as.name(x))), data=Biok_vert_df)
# model2[[x]]<-summary(mod2)$coefficients
emtrend<-emtrends(mod2, pairwise~infusionno, x)
emtrend_contrasts[[x]]<-emtrend$contrasts
emtrend_confint[[x]]<-confint(emtrend)
# mod2_nobmi<-rlmer(substitute(MADRS_Score~age+sex+i*infusionno+(1|patientno)+(1|Site_Location), list(i=as.name(x))), data=Biok_vert_df)
# model2_nobmi[[x]]<-summary(mod2_nobmi)$coefficients
}## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingemtrend_contrasts## $TRP_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.000180 0.000185 Inf -0.973 0.5940
## BL - 3rd -0.000244 0.000187 Inf -1.303 0.3932
## 1st - 3rd -0.000064 0.000186 Inf -0.343 0.9372
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $five_HT_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0029 0.00370 Inf -0.785 0.7122
## BL - 3rd -0.0107 0.00373 Inf -2.866 0.0116
## 1st - 3rd -0.0078 0.00399 Inf -1.955 0.1235
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $KYN_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.00469 0.00450 Inf -1.040 0.5514
## BL - 3rd -0.00611 0.00441 Inf -1.385 0.3486
## 1st - 3rd -0.00142 0.00461 Inf -0.309 0.9487
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $three_HK_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0663 0.134 Inf -0.496 0.8733
## BL - 3rd -0.1336 0.145 Inf -0.921 0.6272
## 1st - 3rd -0.0673 0.160 Inf -0.421 0.9071
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $KYNA_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.1183 0.142 Inf -0.835 0.6814
## BL - 3rd -0.0803 0.128 Inf -0.627 0.8054
## 1st - 3rd 0.0380 0.149 Inf 0.256 0.9646
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $PIC_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.2443 0.109 Inf -2.248 0.0634
## BL - 3rd -0.0939 0.090 Inf -1.044 0.5491
## 1st - 3rd 0.1503 0.129 Inf 1.164 0.4746
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $Quin_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.00363 0.0262 Inf 0.139 0.9895
## BL - 3rd 0.01751 0.0251 Inf 0.698 0.7646
## 1st - 3rd 0.01387 0.0258 Inf 0.537 0.8531
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $AA_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.455 0.334 Inf -1.361 0.3616
## BL - 3rd -0.078 0.401 Inf -0.194 0.9794
## 1st - 3rd 0.377 0.440 Inf 0.856 0.6683
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $KYN_TRP_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st -59.8 111 Inf -0.536 0.8534
## BL - 3rd -91.7 110 Inf -0.835 0.6813
## 1st - 3rd -31.9 107 Inf -0.298 0.9523
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $KYN_SER_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.02829 0.0174 Inf 1.628 0.2339
## BL - 3rd 0.02675 0.0130 Inf 2.052 0.1001
## 1st - 3rd -0.00153 0.0176 Inf -0.087 0.9958
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $QUIN_PIC_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.3292 0.254 Inf 1.299 0.3959
## BL - 3rd 0.2846 0.246 Inf 1.157 0.4793
## 1st - 3rd -0.0447 0.245 Inf -0.182 0.9819
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $QUIN_KYNA_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.578 0.327 Inf 1.769 0.1802
## BL - 3rd 0.359 0.304 Inf 1.182 0.4638
## 1st - 3rd -0.219 0.322 Inf -0.679 0.7755
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $threeHK_KYN_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st 7.83 187 Inf 0.042 0.9990
## BL - 3rd 29.64 186 Inf 0.159 0.9861
## 1st - 3rd 21.82 187 Inf 0.117 0.9925
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $threeHK_KYNA_ratio
## contrast estimate SE df z.ratio p.value
## BL - 1st 1.95 2.49 Inf 0.782 0.7142
## BL - 3rd -1.32 2.41 Inf -0.545 0.8492
## 1st - 3rd -3.27 2.67 Inf -1.222 0.4404
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL1B_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st 2.870 12.19 Inf 0.235 0.9699
## BL - 3rd 2.486 5.22 Inf 0.476 0.8824
## 1st - 3rd -0.384 11.74 Inf -0.033 0.9994
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL1B_pg_mL_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st 12.89 17.63 Inf 0.731 0.7449
## BL - 3rd 1.93 5.86 Inf 0.330 0.9418
## 1st - 3rd -10.96 17.08 Inf -0.641 0.7973
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL2_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0299 0.214 Inf -0.139 0.9894
## BL - 3rd -0.1524 0.208 Inf -0.733 0.7438
## 1st - 3rd -0.1226 0.216 Inf -0.566 0.8380
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL2_pg_mL_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0363 0.217 Inf -0.168 0.9846
## BL - 3rd -0.1332 0.210 Inf -0.634 0.8014
## 1st - 3rd -0.0969 0.219 Inf -0.442 0.8979
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL4_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -30.01 36.7 Inf -0.817 0.6927
## BL - 3rd -37.54 40.6 Inf -0.925 0.6247
## 1st - 3rd -7.54 39.3 Inf -0.192 0.9799
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL4_pg_mL_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st -36.516 36.0 Inf -1.014 0.5679
## BL - 3rd -37.167 40.7 Inf -0.914 0.6314
## 1st - 3rd -0.651 38.5 Inf -0.017 0.9998
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL6_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.070 1.82 Inf -0.589 0.8258
## BL - 3rd -0.768 2.14 Inf -0.359 0.9316
## 1st - 3rd 0.302 2.02 Inf 0.149 0.9878
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL8_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.318 0.639 Inf -2.063 0.0975
## BL - 3rd -0.891 0.674 Inf -1.322 0.3830
## 1st - 3rd 0.427 0.756 Inf 0.564 0.8390
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL10_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.336 0.536 Inf 0.627 0.8052
## BL - 3rd 0.469 0.572 Inf 0.819 0.6911
## 1st - 3rd 0.132 0.599 Inf 0.221 0.9734
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL12p70_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.002773 0.00712 Inf -0.390 0.9197
## BL - 3rd -0.003045 0.00526 Inf -0.579 0.8316
## 1st - 3rd -0.000272 0.00730 Inf -0.037 0.9992
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL12p70_pg_mL_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st -2.99e-03 0.00714 Inf -0.419 0.9080
## BL - 3rd -3.06e-03 0.00528 Inf -0.579 0.8311
## 1st - 3rd -7.19e-05 0.00732 Inf -0.010 0.9999
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL13_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.057479 0.0968 Inf -0.594 0.8233
## BL - 3rd -0.056513 0.0921 Inf -0.614 0.8127
## 1st - 3rd 0.000966 0.0978 Inf 0.010 0.9999
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IL13_pg_mL_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.0671 0.0973 Inf -0.690 0.7695
## BL - 3rd -0.0562 0.0928 Inf -0.606 0.8170
## 1st - 3rd 0.0109 0.0984 Inf 0.111 0.9932
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $TNFa_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.955 2.39 Inf -0.399 0.9158
## BL - 3rd -2.445 2.36 Inf -1.038 0.5527
## 1st - 3rd -1.490 2.31 Inf -0.644 0.7955
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $IFNy_pg_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st 0.2681 0.183 Inf 1.464 0.3082
## BL - 3rd 0.0506 0.208 Inf 0.244 0.9678
## 1st - 3rd -0.2175 0.217 Inf -1.001 0.5764
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $CRP_ng_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -1.94e-04 0.000215 Inf -0.902 0.6391
## BL - 3rd -2.34e-04 0.000243 Inf -0.965 0.5989
## 1st - 3rd -4.04e-05 0.000247 Inf -0.163 0.9854
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $NIC_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -3.22e-03 0.00300 Inf -1.075 0.5299
## BL - 3rd -3.24e-03 0.00338 Inf -0.960 0.6022
## 1st - 3rd -2.05e-05 0.00361 Inf -0.006 1.0000
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $NIC_nM_LLOD
## contrast estimate SE df z.ratio p.value
## BL - 1st -3.22e-03 0.00300 Inf -1.075 0.5299
## BL - 3rd -3.24e-03 0.00338 Inf -0.960 0.6022
## 1st - 3rd -2.05e-05 0.00361 Inf -0.006 1.0000
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $NTA_nM
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.00379 0.00709 Inf -0.534 0.8544
## BL - 3rd -0.00969 0.00724 Inf -1.338 0.3739
## 1st - 3rd -0.00590 0.00786 Inf -0.750 0.7335
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $SAA_ng_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st 5.19e-05 0.000513 Inf 0.101 0.9944
## BL - 3rd -5.90e-04 0.000521 Inf -1.134 0.4930
## 1st - 3rd -6.42e-04 0.000519 Inf -1.237 0.4314
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $VCAM_1_ng_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.00397 0.0166 Inf -0.239 0.9690
## BL - 3rd -0.01008 0.0159 Inf -0.633 0.8021
## 1st - 3rd -0.00611 0.0160 Inf -0.381 0.9231
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## $ICAM_1_ng_mL
## contrast estimate SE df z.ratio p.value
## BL - 1st -0.003287 0.0131 Inf -0.251 0.9658
## BL - 3rd -0.004180 0.0131 Inf -0.318 0.9457
## 1st - 3rd -0.000892 0.0134 Inf -0.067 0.9975
##
## Results are averaged over the levels of: sex
## P value adjustment: tukey method for comparing a family of 3 estimatesemtrend_confint## $TRP_nM
## $TRP_nM$emtrends
## infusionno TRP_nM.trend SE df asymp.LCL asymp.UCL
## BL 3.18e-05 0.000134 Inf -2.31e-04 0.000295
## 1st 2.12e-04 0.000132 Inf -4.77e-05 0.000472
## 3rd 2.76e-04 0.000139 Inf 4.29e-06 0.000547
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $TRP_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.000180 0.000185 Inf -0.000614 0.000254
## BL - 3rd -0.000244 0.000187 Inf -0.000683 0.000195
## 1st - 3rd -0.000064 0.000186 Inf -0.000501 0.000373
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $five_HT_nM
## $five_HT_nM$emtrends
## infusionno five_HT_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.00114 0.00245 Inf -0.00595 0.00367
## 1st 0.00176 0.00281 Inf -0.00375 0.00727
## 3rd 0.00956 0.00291 Inf 0.00386 0.01526
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $five_HT_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.0029 0.00370 Inf -0.0116 0.00576
## BL - 3rd -0.0107 0.00373 Inf -0.0195 -0.00195
## 1st - 3rd -0.0078 0.00399 Inf -0.0171 0.00155
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $KYN_nM
## $KYN_nM$emtrends
## infusionno KYN_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.00088 0.00315 Inf -0.00705 0.00529
## 1st 0.00381 0.00351 Inf -0.00307 0.01068
## 3rd 0.00523 0.00336 Inf -0.00136 0.01182
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $KYN_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.00469 0.00450 Inf -0.0152 0.00587
## BL - 3rd -0.00611 0.00441 Inf -0.0164 0.00423
## 1st - 3rd -0.00142 0.00461 Inf -0.0122 0.00938
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $three_HK_nM
## $three_HK_nM$emtrends
## infusionno three_HK_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.0396 0.0853 Inf -0.207 0.128
## 1st 0.0267 0.1138 Inf -0.196 0.250
## 3rd 0.0940 0.1269 Inf -0.155 0.343
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $three_HK_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.0663 0.134 Inf -0.380 0.247
## BL - 3rd -0.1336 0.145 Inf -0.474 0.207
## 1st - 3rd -0.0673 0.160 Inf -0.442 0.308
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $KYNA_nM
## $KYNA_nM$emtrends
## infusionno KYNA_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.07769 0.0897 Inf -0.253 0.0981
## 1st 0.04059 0.1227 Inf -0.200 0.2811
## 3rd 0.00257 0.1010 Inf -0.195 0.2005
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $KYNA_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.1183 0.142 Inf -0.45 0.214
## BL - 3rd -0.0803 0.128 Inf -0.38 0.220
## 1st - 3rd 0.0380 0.149 Inf -0.31 0.386
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $PIC_nM
## $PIC_nM$emtrends
## infusionno PIC_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.00822 0.0390 Inf -0.0847 0.0682
## 1st 0.23606 0.1017 Inf 0.0366 0.4355
## 3rd 0.08573 0.0814 Inf -0.0738 0.2453
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $PIC_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.2443 0.109 Inf -0.499 0.0104
## BL - 3rd -0.0939 0.090 Inf -0.305 0.1170
## 1st - 3rd 0.1503 0.129 Inf -0.152 0.4529
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $Quin_nM
## $Quin_nM$emtrends
## infusionno Quin_nM.trend SE df asymp.LCL asymp.UCL
## BL 0.000521 0.0186 Inf -0.0360 0.0370
## 1st -0.003113 0.0202 Inf -0.0427 0.0364
## 3rd -0.016985 0.0183 Inf -0.0529 0.0189
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $Quin_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.00363 0.0262 Inf -0.0578 0.0651
## BL - 3rd 0.01751 0.0251 Inf -0.0413 0.0763
## 1st - 3rd 0.01387 0.0258 Inf -0.0467 0.0744
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $AA_nM
## $AA_nM$emtrends
## infusionno AA_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.242 0.202 Inf -0.638 0.155
## 1st 0.213 0.279 Inf -0.334 0.760
## 3rd -0.164 0.357 Inf -0.863 0.535
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $AA_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.455 0.334 Inf -1.237 0.328
## BL - 3rd -0.078 0.401 Inf -1.019 0.863
## 1st - 3rd 0.377 0.440 Inf -0.655 1.408
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $KYN_TRP_ratio
## $KYN_TRP_ratio$emtrends
## infusionno KYN_TRP_ratio.trend SE df asymp.LCL asymp.UCL
## BL -73.8 84.4 Inf -239 91.6
## 1st -14.0 80.8 Inf -172 144.4
## 3rd 17.8 78.8 Inf -137 172.2
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $KYN_TRP_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -59.8 111 Inf -321 202
## BL - 3rd -91.7 110 Inf -349 166
## 1st - 3rd -31.9 107 Inf -283 219
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $KYN_SER_ratio
## $KYN_SER_ratio$emtrends
## infusionno KYN_SER_ratio.trend SE df asymp.LCL asymp.UCL
## BL 0.0151 0.01026 Inf -0.00502 0.03521
## 1st -0.0132 0.01591 Inf -0.04438 0.01800
## 3rd -0.0117 0.00881 Inf -0.02893 0.00561
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $KYN_SER_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.02829 0.0174 Inf -0.0124 0.0690
## BL - 3rd 0.02675 0.0130 Inf -0.0038 0.0573
## 1st - 3rd -0.00153 0.0176 Inf -0.0427 0.0396
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $QUIN_PIC_ratio
## $QUIN_PIC_ratio$emtrends
## infusionno QUIN_PIC_ratio.trend SE df asymp.LCL asymp.UCL
## BL 0.0209 0.185 Inf -0.342 0.3842
## 1st -0.3083 0.182 Inf -0.665 0.0479
## 3rd -0.2636 0.174 Inf -0.604 0.0767
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $QUIN_PIC_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.3292 0.254 Inf -0.265 0.923
## BL - 3rd 0.2846 0.246 Inf -0.292 0.861
## 1st - 3rd -0.0447 0.245 Inf -0.620 0.530
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $QUIN_KYNA_ratio
## $QUIN_KYNA_ratio$emtrends
## infusionno QUIN_KYNA_ratio.trend SE df asymp.LCL asymp.UCL
## BL 0.166 0.221 Inf -0.267 0.5980
## 1st -0.412 0.244 Inf -0.891 0.0665
## 3rd -0.193 0.213 Inf -0.612 0.2246
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $QUIN_KYNA_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.578 0.327 Inf -0.188 1.343
## BL - 3rd 0.359 0.304 Inf -0.353 1.071
## 1st - 3rd -0.219 0.322 Inf -0.973 0.536
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $threeHK_KYN_ratio
## $threeHK_KYN_ratio$emtrends
## infusionno threeHK_KYN_ratio.trend SE df asymp.LCL asymp.UCL
## BL -42.4 133 Inf -304 219
## 1st -50.2 134 Inf -313 213
## 3rd -72.0 133 Inf -332 188
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $threeHK_KYN_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 7.83 187 Inf -430 446
## BL - 3rd 29.64 186 Inf -407 466
## 1st - 3rd 21.82 187 Inf -417 460
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $threeHK_KYNA_ratio
## $threeHK_KYNA_ratio$emtrends
## infusionno threeHK_KYNA_ratio.trend SE df asymp.LCL asymp.UCL
## BL 0.0503 1.61 Inf -3.10 3.20
## 1st -1.9000 1.95 Inf -5.71 1.91
## 3rd 1.3653 1.87 Inf -2.31 5.04
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $threeHK_KYNA_ratio$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 1.95 2.49 Inf -3.90 7.80
## BL - 3rd -1.32 2.41 Inf -6.97 4.34
## 1st - 3rd -3.27 2.67 Inf -9.53 3.00
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL1B_pg_mL
## $IL1B_pg_mL$emtrends
## infusionno IL1B_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -1.03 4.61 Inf -10.07 8.01
## 1st -3.90 11.18 Inf -25.80 18.00
## 3rd -3.52 2.88 Inf -9.17 2.14
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL1B_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 2.870 12.19 Inf -25.70 31.4
## BL - 3rd 2.486 5.22 Inf -9.74 14.7
## 1st - 3rd -0.384 11.74 Inf -27.91 27.1
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL1B_pg_mL_LLOD
## $IL1B_pg_mL_LLOD$emtrends
## infusionno IL1B_pg_mL_LLOD.trend SE df asymp.LCL asymp.UCL
## BL -0.36 5.18 Inf -10.51 9.79
## 1st -13.25 16.83 Inf -46.23 19.73
## 3rd -2.29 2.79 Inf -7.76 3.17
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL1B_pg_mL_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 12.89 17.63 Inf -28.4 54.2
## BL - 3rd 1.93 5.86 Inf -11.8 15.7
## 1st - 3rd -10.96 17.08 Inf -51.0 29.1
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL2_pg_mL
## $IL2_pg_mL$emtrends
## infusionno IL2_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL 0.0937 0.151 Inf -0.2025 0.390
## 1st 0.1236 0.164 Inf -0.1974 0.445
## 3rd 0.2462 0.155 Inf -0.0569 0.549
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL2_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.0299 0.214 Inf -0.532 0.473
## BL - 3rd -0.1524 0.208 Inf -0.640 0.335
## 1st - 3rd -0.1226 0.216 Inf -0.630 0.385
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL2_pg_mL_LLOD
## $IL2_pg_mL_LLOD$emtrends
## infusionno IL2_pg_mL_LLOD.trend SE df asymp.LCL asymp.UCL
## BL 0.0928 0.150 Inf -0.202 0.387
## 1st 0.1291 0.163 Inf -0.191 0.449
## 3rd 0.2260 0.154 Inf -0.076 0.528
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL2_pg_mL_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.0363 0.217 Inf -0.544 0.471
## BL - 3rd -0.1332 0.210 Inf -0.626 0.359
## 1st - 3rd -0.0969 0.219 Inf -0.610 0.417
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL4_pg_mL
## $IL4_pg_mL$emtrends
## infusionno IL4_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -13.2 27.9 Inf -68.0 41.5
## 1st 16.8 25.8 Inf -33.8 67.4
## 3rd 24.3 31.3 Inf -37.1 85.7
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL4_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -30.01 36.7 Inf -116.1 56.1
## BL - 3rd -37.54 40.6 Inf -132.7 57.6
## 1st - 3rd -7.54 39.3 Inf -99.6 84.5
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL4_pg_mL_LLOD
## $IL4_pg_mL_LLOD$emtrends
## infusionno IL4_pg_mL_LLOD.trend SE df asymp.LCL asymp.UCL
## BL -13.0 28.0 Inf -67.8 41.8
## 1st 23.5 24.5 Inf -24.6 71.6
## 3rd 24.2 31.4 Inf -37.4 85.7
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL4_pg_mL_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -36.516 36.0 Inf -120.9 47.9
## BL - 3rd -37.167 40.7 Inf -132.5 58.1
## 1st - 3rd -0.651 38.5 Inf -90.9 89.6
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL6_pg_mL
## $IL6_pg_mL$emtrends
## infusionno IL6_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL 0.701 1.46 Inf -2.159 3.56
## 1st 1.771 1.14 Inf -0.457 4.00
## 3rd 1.469 1.71 Inf -1.886 4.82
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL6_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -1.070 1.82 Inf -5.32 3.18
## BL - 3rd -0.768 2.14 Inf -5.79 4.25
## 1st - 3rd 0.302 2.02 Inf -4.43 5.03
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL8_pg_mL
## $IL8_pg_mL$emtrends
## infusionno IL8_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -0.267 0.391 Inf -1.0326 0.499
## 1st 1.051 0.519 Inf 0.0347 2.068
## 3rd 0.624 0.563 Inf -0.4790 1.728
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL8_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -1.318 0.639 Inf -2.81 0.179
## BL - 3rd -0.891 0.674 Inf -2.47 0.689
## 1st - 3rd 0.427 0.756 Inf -1.35 2.199
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL10_pg_mL
## $IL10_pg_mL$emtrends
## infusionno IL10_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -0.00326 0.361 Inf -0.71 0.704
## 1st -0.33972 0.401 Inf -1.13 0.447
## 3rd -0.47215 0.448 Inf -1.35 0.407
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL10_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.336 0.536 Inf -0.921 1.59
## BL - 3rd 0.469 0.572 Inf -0.873 1.81
## 1st - 3rd 0.132 0.599 Inf -1.270 1.54
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL12p70_pg_mL
## $IL12p70_pg_mL$emtrends
## infusionno IL12p70_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -0.003392 0.00362 Inf -0.01048 0.00369
## 1st -0.000619 0.00629 Inf -0.01294 0.01171
## 3rd -0.000346 0.00398 Inf -0.00815 0.00746
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL12p70_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.002773 0.00712 Inf -0.0195 0.01391
## BL - 3rd -0.003045 0.00526 Inf -0.0154 0.00929
## 1st - 3rd -0.000272 0.00730 Inf -0.0174 0.01683
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL12p70_pg_mL_LLOD
## $IL12p70_pg_mL_LLOD$emtrends
## infusionno IL12p70_pg_mL_LLOD.trend SE df asymp.LCL asymp.UCL
## BL -0.003281 0.00363 Inf -0.01039 0.00382
## 1st -0.000293 0.00630 Inf -0.01264 0.01206
## 3rd -0.000221 0.00399 Inf -0.00805 0.00760
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL12p70_pg_mL_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -2.99e-03 0.00714 Inf -0.0197 0.01374
## BL - 3rd -3.06e-03 0.00528 Inf -0.0154 0.00932
## 1st - 3rd -7.19e-05 0.00732 Inf -0.0172 0.01708
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL13_pg_mL
## $IL13_pg_mL$emtrends
## infusionno IL13_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -0.06336 0.0659 Inf -0.192 0.0657
## 1st -0.00588 0.0738 Inf -0.151 0.1387
## 3rd -0.00685 0.0673 Inf -0.139 0.1251
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL13_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.057479 0.0968 Inf -0.284 0.169
## BL - 3rd -0.056513 0.0921 Inf -0.272 0.159
## 1st - 3rd 0.000966 0.0978 Inf -0.228 0.230
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IL13_pg_mL_LLOD
## $IL13_pg_mL_LLOD$emtrends
## infusionno IL13_pg_mL_LLOD.trend SE df asymp.LCL asymp.UCL
## BL -0.06194 0.0662 Inf -0.192 0.0679
## 1st 0.00518 0.0741 Inf -0.140 0.1504
## 3rd -0.00574 0.0678 Inf -0.139 0.1272
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IL13_pg_mL_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.0671 0.0973 Inf -0.295 0.161
## BL - 3rd -0.0562 0.0928 Inf -0.274 0.161
## 1st - 3rd 0.0109 0.0984 Inf -0.220 0.242
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $TNFa_pg_mL
## $TNFa_pg_mL$emtrends
## infusionno TNFa_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL -2.647 1.75 Inf -6.07 0.777
## 1st -1.692 1.69 Inf -5.00 1.616
## 3rd -0.201 1.64 Inf -3.41 3.009
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $TNFa_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.955 2.39 Inf -6.56 4.65
## BL - 3rd -2.445 2.36 Inf -7.97 3.08
## 1st - 3rd -1.490 2.31 Inf -6.91 3.93
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $IFNy_pg_mL
## $IFNy_pg_mL$emtrends
## infusionno IFNy_pg_mL.trend SE df asymp.LCL asymp.UCL
## BL 0.1424 0.124 Inf -0.0997 0.384
## 1st -0.1257 0.140 Inf -0.3994 0.148
## 3rd 0.0918 0.169 Inf -0.2387 0.422
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $IFNy_pg_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 0.2681 0.183 Inf -0.161 0.697
## BL - 3rd 0.0506 0.208 Inf -0.436 0.537
## 1st - 3rd -0.2175 0.217 Inf -0.727 0.292
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $CRP_ng_mL
## $CRP_ng_mL$emtrends
## infusionno CRP_ng_mL.trend SE df asymp.LCL asymp.UCL
## BL -4.33e-05 0.000154 Inf -0.000345 0.000258
## 1st 1.50e-04 0.000162 Inf -0.000166 0.000467
## 3rd 1.91e-04 0.000201 Inf -0.000203 0.000584
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $CRP_ng_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -1.94e-04 0.000215 Inf -0.000697 0.000310
## BL - 3rd -2.34e-04 0.000243 Inf -0.000802 0.000334
## 1st - 3rd -4.04e-05 0.000247 Inf -0.000620 0.000540
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $NIC_nM
## $NIC_nM$emtrends
## infusionno NIC_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.000999 0.00192 Inf -0.00477 0.00277
## 1st 0.002223 0.00232 Inf -0.00231 0.00676
## 3rd 0.002244 0.00279 Inf -0.00323 0.00771
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $NIC_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -3.22e-03 0.00300 Inf -0.01025 0.00381
## BL - 3rd -3.24e-03 0.00338 Inf -0.01116 0.00467
## 1st - 3rd -2.05e-05 0.00361 Inf -0.00849 0.00845
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $NIC_nM_LLOD
## $NIC_nM_LLOD$emtrends
## infusionno NIC_nM_LLOD.trend SE df asymp.LCL asymp.UCL
## BL -0.000999 0.00192 Inf -0.00477 0.00277
## 1st 0.002223 0.00232 Inf -0.00231 0.00676
## 3rd 0.002244 0.00279 Inf -0.00323 0.00771
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $NIC_nM_LLOD$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -3.22e-03 0.00300 Inf -0.01025 0.00381
## BL - 3rd -3.24e-03 0.00338 Inf -0.01116 0.00467
## 1st - 3rd -2.05e-05 0.00361 Inf -0.00849 0.00845
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $NTA_nM
## $NTA_nM$emtrends
## infusionno NTA_nM.trend SE df asymp.LCL asymp.UCL
## BL -0.003672 0.00455 Inf -0.01260 0.00526
## 1st 0.000118 0.00547 Inf -0.01060 0.01083
## 3rd 0.006015 0.00575 Inf -0.00525 0.01729
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $NTA_nM$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.00379 0.00709 Inf -0.0204 0.01283
## BL - 3rd -0.00969 0.00724 Inf -0.0267 0.00728
## 1st - 3rd -0.00590 0.00786 Inf -0.0243 0.01253
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $SAA_ng_mL
## $SAA_ng_mL$emtrends
## infusionno SAA_ng_mL.trend SE df asymp.LCL asymp.UCL
## BL 6.37e-05 0.000381 Inf -0.000683 0.000811
## 1st 1.17e-05 0.000379 Inf -0.000731 0.000754
## 3rd 6.54e-04 0.000397 Inf -0.000123 0.001432
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $SAA_ng_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st 5.19e-05 0.000513 Inf -0.00115 0.001255
## BL - 3rd -5.90e-04 0.000521 Inf -0.00181 0.000630
## 1st - 3rd -6.42e-04 0.000519 Inf -0.00186 0.000575
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $VCAM_1_ng_mL
## $VCAM_1_ng_mL$emtrends
## infusionno VCAM_1_ng_mL.trend SE df asymp.LCL asymp.UCL
## BL -0.00150 0.0118 Inf -0.0246 0.0216
## 1st 0.00247 0.0120 Inf -0.0210 0.0259
## 3rd 0.00858 0.0110 Inf -0.0130 0.0302
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $VCAM_1_ng_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.00397 0.0166 Inf -0.0429 0.0349
## BL - 3rd -0.01008 0.0159 Inf -0.0474 0.0273
## 1st - 3rd -0.00611 0.0160 Inf -0.0437 0.0315
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
##
##
## $ICAM_1_ng_mL
## $ICAM_1_ng_mL$emtrends
## infusionno ICAM_1_ng_mL.trend SE df asymp.LCL asymp.UCL
## BL 0.00835 0.00938 Inf -0.01004 0.0267
## 1st 0.01164 0.00969 Inf -0.00736 0.0306
## 3rd 0.01253 0.00963 Inf -0.00635 0.0314
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
##
## $ICAM_1_ng_mL$contrasts
## contrast estimate SE df asymp.LCL asymp.UCL
## BL - 1st -0.003287 0.0131 Inf -0.0340 0.0274
## BL - 3rd -0.004180 0.0131 Inf -0.0349 0.0266
## 1st - 3rd -0.000892 0.0134 Inf -0.0322 0.0304
##
## Results are averaged over the levels of: sex
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimatesemtrends(rlmer,pairwise~infusionno,”TRP_nM”) The $contrats portion of this will let us know if MADRS:Marker correlations change at any timepoint Also this confint(emtrends(rlmer,pairwise~infusionno,”TRP_nM”)) will help us determine which markers are correlated with MADRS score within each timepoint.
11 Model 3: MADRS_3rd by Biomarker_BL + MADRS_BL
Biok_wide_new_bx<-Biok_wide_new %>%
select(-contains("MADRS"), -contains("BSS"), -contains("_1st"), -contains("_3rd"),-sex, -age, -BMI, -race, -patientno, -Remission) %>% names()
model3<-list()
model3_nobmi<-list()
for (x in Biok_wide_new_bx){
mod3<-lm(substitute(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+i, list(i=as.name(x))), data=Biok_wide_new)
mod3_nobmi<-lm(substitute(MADRS_Score_3rd~age+sex+MADRS_Score_BL+i, list(i=as.name(x))), data=Biok_wide_new)
# model3[[x]]<-summary(mod3)$coefficients[,4]
# model3_nobmi[[x]]<-summary(mod3_nobmi)$coefficients[,4]
model3[[x]]<-summary(mod3)$coefficients
model3_nobmi[[x]]<-summary(mod3_nobmi)$coefficients
}
model3## $TRP_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.396762e+01 8.1228237326 -1.7195520 0.09034704
## age 4.179664e-02 0.0670903528 0.6229902 0.53550469
## sexfemale 3.041263e+00 1.7926754348 1.6964938 0.09465160
## BMI 5.317539e-02 0.1519152765 0.3500332 0.72746313
## MADRS_Score_BL 2.914078e-01 0.1471370494 1.9805197 0.05194696
## TRP_nM_BL 3.310841e-04 0.0001489956 2.2221062 0.02981799
##
## $five_HT_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.63252324 6.75073813 -1.1306205 0.26243408
## age 0.05679040 0.06755990 0.8405933 0.40370541
## sexfemale 1.49991029 1.73133855 0.8663299 0.38954556
## BMI 0.05471647 0.15109741 0.3621271 0.71845003
## MADRS_Score_BL 0.35004726 0.14676261 2.3851255 0.02004546
## five_HT_nM_BL 0.00640233 0.00268956 2.3804377 0.02028071
##
## $KYN_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.588392712 7.08316683 -0.93014789 0.35578980
## age -0.007690056 0.07266740 -0.10582540 0.91605188
## sexfemale 2.545490779 1.81067161 1.40582685 0.16461196
## BMI -0.006840197 0.15586869 -0.04388435 0.96513322
## MADRS_Score_BL 0.314930690 0.15000704 2.09943938 0.03972686
## KYN_nM_BL 0.005475453 0.00387667 1.41241143 0.16267321
##
## $three_HK_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.38171551 6.88393075 -0.4912478 0.62493168
## age 0.02676339 0.06954423 0.3848398 0.70163203
## sexfemale 1.98540031 1.79362340 1.1069215 0.27247106
## BMI 0.01777757 0.15937982 0.1115422 0.91153550
## MADRS_Score_BL 0.31713742 0.15362581 2.0643498 0.04304262
## three_HK_nM_BL 0.01322423 0.10026952 0.1318868 0.89548758
##
## $KYNA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.87988061 7.36405858 -0.3910725 0.69704257
## age 0.02910535 0.07060239 0.4122431 0.68153883
## sexfemale 1.92789327 1.85718947 1.0380703 0.30314345
## BMI 0.02118385 0.15696637 0.1349579 0.89306875
## MADRS_Score_BL 0.31085312 0.15545005 1.9996977 0.04978139
## KYNA_nM_BL -0.01289453 0.11029460 -0.1169099 0.90729756
##
## $PIC_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.69043621 6.88842851 -0.39057329 0.69740973
## age 0.03125663 0.06983331 0.44758919 0.65596104
## sexfemale 2.00404601 1.79222852 1.11818666 0.26766700
## BMI 0.01547450 0.15745976 0.09827591 0.92202036
## MADRS_Score_BL 0.30875099 0.15279225 2.02072412 0.04749662
## PIC_nM_BL -0.01744039 0.04312656 -0.40440011 0.68726684
##
## $Quin_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.85659070 7.05474999 -0.1214204 0.90373847
## age 0.04572793 0.07069860 0.6468011 0.52007375
## sexfemale 1.37174220 1.86497465 0.7355286 0.46470469
## BMI 0.03933210 0.15641473 0.2514603 0.80226390
## MADRS_Score_BL 0.32157996 0.15108518 2.1284679 0.03715378
## Quin_nM_BL -0.02373179 0.02186506 -1.0853750 0.28182706
##
## $AA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.70741738 6.54162059 -0.4138756 0.68034891
## age 0.05198952 0.06785436 0.7661928 0.44637845
## sexfemale 2.24467975 1.73579127 1.2931738 0.20059943
## BMI 0.12135123 0.15840141 0.7660994 0.44643358
## MADRS_Score_BL 0.26029793 0.14915927 1.7451007 0.08576719
## AA_nM_BL -0.49032451 0.22658529 -2.1639732 0.03420502
##
## $KYN_TRP_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.89383580 7.06298488 -0.4097185 0.68338060
## age 0.03194778 0.07450963 0.4287738 0.66952763
## sexfemale 1.99219920 1.79416400 1.1103774 0.27099089
## BMI 0.02694105 0.16060067 0.1677518 0.86730754
## MADRS_Score_BL 0.31157712 0.15335958 2.0316769 0.04634262
## KYN_TRP_ratio_BL -16.58757345 102.78713100 -0.1613779 0.87230412
##
## $KYN_SER_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.89516457 6.84606171 -0.7150337 0.47718858
## age 0.03912899 0.06925148 0.5650275 0.57403040
## sexfemale 2.20713892 1.78429670 1.2369798 0.22061435
## BMI 0.02051913 0.15526841 0.1321526 0.89527815
## MADRS_Score_BL 0.36775974 0.15720962 2.3392954 0.02245341
## KYN_SER_ratio_BL -0.01352328 0.01138615 -1.1876961 0.23934239
##
## $QUIN_PIC_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.76025019 6.73532899 -0.5582875 0.57859603
## age 0.03758971 0.06939909 0.5416456 0.58994341
## sexfemale 2.17833575 1.78843136 1.2180147 0.22768979
## BMI 0.05985463 0.15993415 0.3742455 0.70945881
## MADRS_Score_BL 0.34216657 0.15335170 2.2312539 0.02917473
## QUIN_PIC_ratio_BL -0.21745779 0.20819200 -1.0445060 0.30018057
##
## $QUIN_KYNA_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.98611505 6.76227692 -0.4415843 0.66027849
## age 0.02865775 0.06912639 0.4145704 0.67984275
## sexfemale 2.14197733 1.80638193 1.1857832 0.24009180
## BMI 0.03213408 0.15744043 0.2041031 0.83892057
## MADRS_Score_BL 0.34165695 0.15803555 2.1618993 0.03437146
## QUIN_KYNA_ratio_BL -0.18184659 0.29258644 -0.6215141 0.53646907
##
## $threeHK_KYN_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.60858425 7.29999938 -0.08336771 0.93381937
## age 0.01878377 0.06950729 0.27024179 0.78784379
## sexfemale 2.19928810 1.79730196 1.22366088 0.22556624
## BMI 0.04522989 0.15806284 0.28615135 0.77568642
## MADRS_Score_BL 0.29137596 0.15339262 1.89954349 0.06200119
## threeHK_KYN_ratio_BL -137.21891617 148.96524565 -0.92114718 0.36043292
##
## $threeHK_KYNA_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.26389662 6.72836405 -0.48509513 0.62926523
## age 0.02850468 0.06886739 0.41390671 0.68032626
## sexfemale 1.51822871 1.85307951 0.81930036 0.41565566
## BMI -0.01145057 0.16001020 -0.07156152 0.94317402
## MADRS_Score_BL 0.30246451 0.15190023 1.99120506 0.05073063
## threeHK_KYNA_ratio_BL 1.70469966 1.85758127 0.91769856 0.36222220
##
## $IL1B_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.11086256 12.53070121 0.2482593 0.8089576
## age -0.06081348 0.09780974 -0.6217528 0.5480164
## sexfemale -3.35825420 2.65579847 -1.2644989 0.2347267
## BMI -0.03428727 0.26161366 -0.1310607 0.8983265
## MADRS_Score_BL 0.28791096 0.24265070 1.1865243 0.2628392
## IL1B_pg_mL_BL 4.55287631 2.98492361 1.5252907 0.1581699
##
## $IL1B_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.07972810 7.11564261 -0.5733464 0.56841941
## age 0.02751741 0.06922851 0.3974866 0.69233129
## sexfemale 1.91473132 1.80054277 1.0634190 0.29158803
## BMI 0.02374712 0.15689385 0.1513579 0.88016952
## MADRS_Score_BL 0.31777959 0.15237977 2.0854447 0.04102171
## IL1B_pg_mL_LLOD_BL 2.18458359 5.59997969 0.3901056 0.69775378
##
## $IL2_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9815982 7.37724238 -0.1330576 0.89484996
## age 0.0958955 0.08075374 1.1875053 0.24239844
## sexfemale -1.3524518 1.97905588 -0.6833823 0.49851119
## BMI -0.1301561 0.19321390 -0.6736372 0.50461903
## MADRS_Score_BL 0.3294784 0.15151026 2.1746276 0.03595144
## IL2_pg_mL_BL 0.2506601 0.14278872 1.7554616 0.08723961
##
## $IL2_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.59807980 6.69233622 -0.5376418 0.59268900
## age 0.03597533 0.06873831 0.5233665 0.60252672
## sexfemale 1.83649361 1.77464151 1.0348533 0.30463195
## BMI 0.03566807 0.15535658 0.2295884 0.81914380
## MADRS_Score_BL 0.29617683 0.15105012 1.9607852 0.05425905
## IL2_pg_mL_LLOD_BL 0.20842750 0.16149755 1.2905923 0.20148814
##
## $IL4_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.048480105 7.70547048 -0.6551813 0.51469916
## age 0.037291461 0.07193747 0.5183871 0.60597585
## sexfemale 1.904431353 1.79766795 1.0593899 0.29340416
## BMI 0.006255084 0.15964547 0.0391811 0.96886797
## MADRS_Score_BL 0.332501623 0.15633530 2.1268493 0.03729334
## IL4_pg_mL_BL 15.651769690 31.63521051 0.4947579 0.62246527
##
## $IL4_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.048480105 7.70547048 -0.6551813 0.51469916
## age 0.037291461 0.07193747 0.5183871 0.60597585
## sexfemale 1.904431353 1.79766795 1.0593899 0.29340416
## BMI 0.006255084 0.15964547 0.0391811 0.96886797
## MADRS_Score_BL 0.332501623 0.15633530 2.1268493 0.03729334
## IL4_pg_mL_LLOD_BL 15.651769690 31.63521051 0.4947579 0.62246527
##
## $IL6_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.11643560 6.90668098 -0.5960078 0.5532722
## age 0.03443219 0.07000074 0.4918832 0.6244849
## sexfemale 2.24884767 1.83901612 1.2228537 0.2258689
## BMI 0.06187122 0.16961613 0.3647720 0.7164842
## MADRS_Score_BL 0.32823815 0.15349353 2.1384495 0.0363032
## IL6_pg_mL_BL -1.08820078 1.76001557 -0.6182904 0.5385782
##
## $IL8_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.82617510 7.11305925 -0.5379085 0.5925059
## age 0.02371607 0.07061518 0.3358494 0.7380830
## sexfemale 2.05530880 1.81093437 1.1349438 0.2606316
## BMI 0.02283202 0.15695406 0.1454695 0.8847974
## MADRS_Score_BL 0.31788370 0.15272475 2.0814158 0.0414012
## IL8_pg_mL_BL 0.12566987 0.45224933 0.2778774 0.7820022
##
## $IL10_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.18095466 6.65549107 -0.4779444 0.63431826
## age 0.03700499 0.06840489 0.5409700 0.59040626
## sexfemale 2.29688663 1.77508001 1.2939623 0.20032856
## BMI 0.01669625 0.15429187 0.1082121 0.91416597
## MADRS_Score_BL 0.30970712 0.14973188 2.0684113 0.04264696
## IL10_pg_mL_BL -0.58014509 0.38529472 -1.5057177 0.13705952
##
## $IL12p70_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.587447668 6.795085553 -0.52794739 0.59936165
## age 0.036257652 0.071165971 0.50948018 0.61216800
## sexfemale 2.138294014 1.814343512 1.17854971 0.24294085
## BMI 0.005676360 0.159533076 0.03558109 0.97172714
## MADRS_Score_BL 0.324523302 0.153221561 2.11800023 0.03806439
## IL12p70_pg_mL_BL 0.002096697 0.004025941 0.52079670 0.60430566
##
## $IL12p70_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.587447668 6.795085553 -0.52794739 0.59936165
## age 0.036257652 0.071165971 0.50948018 0.61216800
## sexfemale 2.138294014 1.814343512 1.17854971 0.24294085
## BMI 0.005676360 0.159533076 0.03558109 0.97172714
## MADRS_Score_BL 0.324523302 0.153221561 2.11800023 0.03806439
## IL12p70_pg_mL_LLOD_BL 0.002096697 0.004025941 0.52079670 0.60430566
##
## $IL13_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.200874311 6.90918512 -0.4632781 0.64476153
## age 0.035035021 0.07181870 0.4878259 0.62736661
## sexfemale 1.912385079 1.84453069 1.0367868 0.30379841
## BMI 0.004513109 0.16048036 0.0281225 0.97765334
## MADRS_Score_BL 0.317423997 0.15453581 2.0540482 0.04412592
## IL13_pg_mL_BL 0.033951121 0.07441843 0.4562193 0.64980121
##
## $IL13_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.755304691 6.83673314 -0.54928350 0.58472244
## age 0.036910053 0.07148447 0.51633668 0.60739880
## sexfemale 2.128446038 1.81149130 1.17496895 0.24436016
## BMI 0.004895886 0.15984304 0.03062934 0.97566046
## MADRS_Score_BL 0.325548445 0.15349127 2.12095744 0.03780519
## IL13_pg_mL_LLOD_BL 0.038361309 0.07385905 0.51938534 0.60528369
##
## $TNFa_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.16933336 7.66672345 -0.5438221 0.5884534
## age 0.02562159 0.06964844 0.3678702 0.7141838
## sexfemale 2.09137969 1.83820633 1.1377285 0.2594753
## BMI 0.02018692 0.15695406 0.1286167 0.8980643
## MADRS_Score_BL 0.32249785 0.15523460 2.0774869 0.0417742
## TNFa_pg_mL_BL 0.53226141 2.01496816 0.2641538 0.7925102
##
## $IFNy_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.6292929633 6.76054409 -0.388917361 0.69862823
## age 0.0307674720 0.06896274 0.446146332 0.65699737
## sexfemale 1.8979394667 1.78497884 1.063284011 0.29164873
## BMI -0.0005491233 0.15784519 -0.003478873 0.99723509
## MADRS_Score_BL 0.3456727424 0.15521950 2.226993009 0.02947281
## IFNy_pg_mL_BL -0.1246916174 0.13756804 -0.906399613 0.36812436
##
## $CRP_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7813590219 6.8839998939 -0.2587680 0.79664468
## age 0.0304355096 0.0688711642 0.4419195 0.66003716
## sexfemale 1.7917329459 1.7917850711 0.9999709 0.32109046
## BMI -0.0419214847 0.1689418180 -0.2481416 0.80481935
## MADRS_Score_BL 0.3094710001 0.1513057215 2.0453357 0.04493743
## CRP_ng_mL_BL 0.0001687193 0.0001738043 0.9707430 0.33533081
##
## $NIC_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.381528093 6.760945340 -0.5001561 0.61868064
## age 0.028147308 0.069141571 0.4070967 0.68529531
## sexfemale 1.875668548 1.799218450 1.0424907 0.30110624
## BMI 0.016876143 0.156767692 0.1076506 0.91460959
## MADRS_Score_BL 0.324384348 0.152912368 2.1213742 0.03776878
## NIC_nM_BL 0.001207579 0.002104615 0.5737765 0.56813002
##
## $NIC_nM_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.381520731 6.760945663 -0.5001550 0.6186814
## age 0.028147108 0.069141573 0.4070938 0.6852974
## sexfemale 1.875667914 1.799219104 1.0424900 0.3011066
## BMI 0.016876353 0.156767695 0.1076520 0.9146085
## MADRS_Score_BL 0.324384191 0.152912388 2.1213729 0.0377689
## NIC_nM_LLOD_BL 0.001207557 0.002104616 0.5737659 0.5681372
##
## $NTA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.418335166 7.48642642 -0.7237545 0.47185367
## age 0.035720353 0.07010948 0.5094939 0.61215841
## sexfemale 2.172853110 1.80891166 1.2011936 0.23410267
## BMI 0.030710230 0.15700237 0.1956036 0.84554014
## MADRS_Score_BL 0.324482897 0.15249448 2.1278337 0.03720841
## NTA_nM_BL 0.003462993 0.00510282 0.6786431 0.49981075
##
## $SAA_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.4765203774 6.9171637355 -0.35802541 0.72150246
## age 0.0260651955 0.0692344875 0.37647705 0.70780753
## sexfemale 1.7863089736 1.8328207457 0.97462285 0.33341687
## BMI -0.0132857740 0.1711293592 -0.07763585 0.93835993
## MADRS_Score_BL 0.3071804954 0.1527144231 2.01147010 0.04849082
## SAA_ng_mL_BL 0.0002218888 0.0004401364 0.50413643 0.61589669
##
## $VCAM_1_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.471003878 8.88657677 -0.1655310 0.86904784
## age 0.023636516 0.07043971 0.3355567 0.73830270
## sexfemale 1.867636448 1.83327623 1.0187425 0.31216093
## BMI 0.023362329 0.15698399 0.1488198 0.88216375
## MADRS_Score_BL 0.306464718 0.15449751 1.9836224 0.05159122
## VCAM_1_ng_mL_BL -0.004247904 0.01400303 -0.3033561 0.76260243
##
## $ICAM_1_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.48884313 7.06911764 -0.7764538 0.44034131
## age 0.02315177 0.06887415 0.3361460 0.73786039
## sexfemale 2.29665702 1.80480964 1.2725204 0.20779204
## BMI 0.01718406 0.15573712 0.1103402 0.91248488
## MADRS_Score_BL 0.28079018 0.15457825 1.8164921 0.07397828
## ICAM_1_ng_mL_BL 0.01063176 0.01031879 1.0303301 0.30673318model3_nobmi## $TRP_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.259723e+01 7.068875006 -1.7820695 0.07940858
## age 4.782093e-02 0.064406093 0.7424907 0.46046494
## sexfemale 2.995822e+00 1.775859301 1.6869703 0.09640307
## MADRS_Score_BL 2.933018e-01 0.146041675 2.0083433 0.04876535
## TRP_nM_BL 3.261805e-04 0.000147331 2.2139309 0.03034763
##
## $five_HT_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.323749780 5.663565895 -1.1165668 0.26829014
## age 0.062796642 0.065053133 0.9653131 0.33796585
## sexfemale 1.476070200 1.718484394 0.8589372 0.39353174
## MADRS_Score_BL 0.351144325 0.145747355 2.4092672 0.01882651
## five_HT_nM_BL 0.006312212 0.002660063 2.3729556 0.02061377
##
## $KYN_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.729540045 6.262174347 -1.0746331 0.28651205
## age -0.008345423 0.070568194 -0.1182604 0.90622604
## sexfemale 2.547052605 1.796369315 1.4178892 0.16099949
## MADRS_Score_BL 0.314730800 0.148782270 2.1153784 0.03823487
## KYN_nM_BL 0.005453602 0.003814929 1.4295421 0.15763736
##
## $three_HK_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.00962657 5.97575411 -0.5036396 0.61621732
## age 0.02869020 0.06685097 0.4291665 0.66922120
## sexfemale 1.97583482 1.77791017 1.1113243 0.27052262
## MADRS_Score_BL 0.31803524 0.15224491 2.0889712 0.04063016
## three_HK_nM_BL 0.01517094 0.09798599 0.1548276 0.87743697
##
## $KYNA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.38778792 6.34947192 -0.3760609 0.70809629
## age 0.03163618 0.06755037 0.4683346 0.64111206
## sexfemale 1.91502985 1.84068132 1.0403919 0.30201239
## MADRS_Score_BL 0.31141916 0.15421543 2.0193774 0.04757525
## KYNA_nM_BL -0.01309930 0.10944810 -0.1196850 0.90510182
##
## $PIC_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.32593577 5.76027525 -0.4037890 0.68769328
## age 0.03315619 0.06659188 0.4979014 0.62023394
## sexfemale 1.99582919 1.77658650 1.1234067 0.26539692
## MADRS_Score_BL 0.30907221 0.15158912 2.0388813 0.04553269
## PIC_nM_BL -0.01783720 0.04260878 -0.4186274 0.67686849
##
## $Quin_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02044511 6.17696479 -0.003309896 0.99736923
## age 0.04988518 0.06824130 0.731011495 0.46739916
## sexfemale 1.36471917 1.85127942 0.737176223 0.46366791
## MADRS_Score_BL 0.32255220 0.14994340 2.151159671 0.03518582
## Quin_nM_BL -0.02315221 0.02158599 -1.072557159 0.28743586
##
## $AA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.20837636 5.6522219 -0.03686627 0.97070462
## age 0.06260267 0.0662137 0.94546407 0.34792345
## sexfemale 2.15504810 1.7263319 1.24833941 0.21638467
## MADRS_Score_BL 0.26916008 0.1482367 1.81574489 0.07402210
## AA_nM_BL -0.43972615 0.2160559 -2.03524224 0.04590795
##
## $KYN_TRP_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.37426883 6.30003428 -0.3768660 0.7075007
## age 0.03401586 0.07293123 0.4664101 0.6424814
## sexfemale 1.97589490 1.77808588 1.1112483 0.2705551
## MADRS_Score_BL 0.31297725 0.15198318 2.0592887 0.0434769
## KYN_TRP_ratio_BL -12.92528731 99.68818984 -0.1296572 0.8972381
##
## $KYN_SER_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.42456758 5.80267044 -0.7625054 0.4485167
## age 0.04156298 0.06625088 0.6273574 0.5326226
## sexfemale 2.19566670 1.76866271 1.2414276 0.2189092
## MADRS_Score_BL 0.36839350 0.15594430 2.3623402 0.0211641
## KYN_SER_ratio_BL -0.01353077 0.01129962 -1.1974529 0.2354788
##
## $QUIN_PIC_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.41285518 5.65454097 -0.4267111 0.67099981
## age 0.04346763 0.06714983 0.6473230 0.51970264
## sexfemale 2.13030248 1.77198087 1.2022153 0.23364160
## MADRS_Score_BL 0.34155526 0.15232527 2.2422758 0.02836161
## QUIN_PIC_ratio_BL -0.19953907 0.20126686 -0.9914154 0.32515915
##
## $QUIN_KYNA_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.2648221 5.72271064 -0.3957604 0.69357785
## age 0.0323736 0.06619247 0.4890829 0.62642935
## sexfemale 2.1183700 1.78933670 1.1838856 0.24076977
## MADRS_Score_BL 0.3416165 0.15686609 2.1777589 0.03305891
## QUIN_KYNA_ratio_BL -0.1753178 0.28868070 -0.6073071 0.54576293
##
## $threeHK_KYN_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27057732 6.57504195 0.04115218 0.9673007
## age 0.02443601 0.06616923 0.36929575 0.7131076
## sexfemale 2.16349114 1.78023547 1.21528369 0.2286538
## MADRS_Score_BL 0.29384566 0.15206417 1.93237941 0.0576726
## threeHK_KYN_ratio_BL -130.25293691 145.92107330 -0.89262595 0.3753503
##
## $threeHK_KYNA_ratio_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.51303813 5.71337423 -0.6148798 0.54078079
## age 0.02720087 0.06590342 0.4127383 0.68115662
## sexfemale 1.53251305 1.82814411 0.8382890 0.40494056
## MADRS_Score_BL 0.30235417 0.15072550 2.0059921 0.04902221
## threeHK_KYNA_ratio_BL 1.67492806 1.79648645 0.9323355 0.35461346
##
## $IL1B_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.94840463 8.4467840 0.2306682 0.8218065
## age -0.06432802 0.0897615 -0.7166549 0.4885241
## sexfemale -3.45150235 2.4417385 -1.4135430 0.1851657
## MADRS_Score_BL 0.30153935 0.2092171 1.4412748 0.1773626
## IL1B_pg_mL_BL 4.65878301 2.7420909 1.6989893 0.1173910
##
## $IL1B_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.52216904 6.04197722 -0.5829497 0.56194421
## age 0.03032311 0.06619738 0.4580712 0.64842901
## sexfemale 1.90234800 1.78511278 1.0656738 0.29051364
## MADRS_Score_BL 0.31842971 0.15117004 2.1064340 0.03903206
## IL1B_pg_mL_LLOD_BL 2.15258209 5.55376780 0.3875895 0.69958616
##
## $IL2_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.66265246 6.16791543 -0.5938234 0.55605914
## age 0.08433111 0.07835339 1.0762918 0.28840993
## sexfemale -1.40935759 1.96335717 -0.7178305 0.47713997
## MADRS_Score_BL 0.31240954 0.14832673 2.1062255 0.04167179
## IL2_pg_mL_BL 0.26292895 0.14062719 1.8696879 0.06904819
##
## $IL2_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.77774855 5.61729527 -0.4944993 0.62262084
## age 0.04006734 0.06590189 0.6079847 0.54531620
## sexfemale 1.81830336 1.75990594 1.0331821 0.30534756
## MADRS_Score_BL 0.29745317 0.14984381 1.9850882 0.05135752
## IL2_pg_mL_LLOD_BL 0.20579568 0.15991206 1.2869303 0.20268283
##
## $IL4_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.93790457 7.1147701 -0.6940357 0.49013289
## age 0.03815275 0.0679684 0.5613307 0.57650232
## sexfemale 1.89981288 1.7799686 1.0673294 0.28977130
## MADRS_Score_BL 0.33295323 0.1547077 2.1521434 0.03510509
## IL4_pg_mL_BL 15.89000399 30.8060198 0.5158084 0.60773837
##
## $IL4_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.93790457 7.1147701 -0.6940357 0.49013289
## age 0.03815275 0.0679684 0.5613307 0.57650232
## sexfemale 1.89981288 1.7799686 1.0673294 0.28977130
## MADRS_Score_BL 0.33295323 0.1547077 2.1521434 0.03510509
## IL4_pg_mL_LLOD_BL 15.89000399 30.8060198 0.5158084 0.60773837
##
## $IL6_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.70166264 5.67659952 -0.4759298 0.63572018
## age 0.03909381 0.06836381 0.5718495 0.56939635
## sexfemale 2.15888097 1.81020824 1.1926147 0.23735582
## MADRS_Score_BL 0.32666145 0.15240601 2.1433633 0.03583147
## IL6_pg_mL_BL -0.84066672 1.61306677 -0.5211605 0.60402606
##
## $IL8_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.29183959 6.04521446 -0.5445364 0.58793572
## age 0.02647845 0.06749994 0.3922737 0.69613930
## sexfemale 2.04122779 1.79467791 1.1373783 0.25955555
## MADRS_Score_BL 0.31849852 0.15151239 2.1021285 0.03942092
## IL8_pg_mL_BL 0.12356452 0.44860130 0.2754440 0.78384892
##
## $IL10_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.79737419 5.59000500 -0.5004243 0.61846656
## age 0.03899341 0.06538784 0.5963404 0.55301911
## sexfemale 2.28791359 1.75961071 1.3002385 0.19811085
## MADRS_Score_BL 0.31019161 0.14852278 2.0885120 0.04067294
## IL10_pg_mL_BL -0.58099577 0.38227481 -1.5198380 0.13340137
##
## $IL12p70_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.466481542 5.838077681 -0.5937711 0.55472607
## age 0.037018177 0.067356746 0.5495838 0.58448826
## sexfemale 2.137198554 1.800091511 1.1872722 0.23944111
## MADRS_Score_BL 0.324814518 0.151822804 2.1394317 0.03616096
## IL12p70_pg_mL_BL 0.002123869 0.003922363 0.5414768 0.59003021
##
## $IL12p70_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.466481542 5.838077681 -0.5937711 0.55472607
## age 0.037018177 0.067356746 0.5495838 0.58448826
## sexfemale 2.137198554 1.800091511 1.1872722 0.23944111
## MADRS_Score_BL 0.324814518 0.151822804 2.1394317 0.03616096
## IL12p70_pg_mL_LLOD_BL 0.002123869 0.003922363 0.5414768 0.59003021
##
## $IL13_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.10702106 6.00232041 -0.5176367 0.60649648
## age 0.03564911 0.06788236 0.5251602 0.60128652
## sexfemale 1.91146877 1.82978949 1.0446386 0.30011973
## MADRS_Score_BL 0.31767033 0.15307822 2.0752157 0.04199115
## IL13_pg_mL_BL 0.03436600 0.07236991 0.4748658 0.63649911
##
## $IL13_pg_mL_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.65357191 5.92960864 -0.6161573 0.53994254
## age 0.03757651 0.06756711 0.5561362 0.58002728
## sexfemale 2.12748386 1.79724562 1.1837469 0.24082432
## MADRS_Score_BL 0.32581687 0.15205866 2.1427052 0.03588644
## IL13_pg_mL_LLOD_BL 0.03881203 0.07181994 0.5404074 0.59076308
##
## $TNFa_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.71980764 6.77189500 -0.5493008 0.58468128
## age 0.02797976 0.06668156 0.4196026 0.67615940
## sexfemale 2.08155451 1.82267125 1.1420351 0.25762902
## MADRS_Score_BL 0.32320935 0.15395791 2.0993358 0.03967494
## TNFa_pg_mL_BL 0.54007101 1.99875857 0.2702032 0.78785998
##
## $IFNy_pg_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.64196136 5.65200131 -0.4674382 0.64174972
## age 0.03070213 0.06584321 0.4662915 0.64256580
## sexfemale 1.89829334 1.76831688 1.0735029 0.28701473
## MADRS_Score_BL 0.34563856 0.15371198 2.2486117 0.02793366
## IFNy_pg_mL_BL -0.12461807 0.13488439 -0.9238880 0.35896172
##
## $CRP_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.7428400710 5.6487622234 -0.4855648 0.62890852
## age 0.0259289978 0.0659522219 0.3931482 0.69549654
## sexfemale 1.8308947880 1.7718902557 1.0333003 0.30529268
## MADRS_Score_BL 0.3089160464 0.1501931166 2.0567923 0.04372399
## CRP_ng_mL_BL 0.0001520577 0.0001591488 0.9554432 0.34289364
##
## $NIC_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.996258698 5.692243733 -0.5263757 0.60041880
## age 0.030144683 0.066097184 0.4560661 0.64986262
## sexfemale 1.865123625 1.782838193 1.0461542 0.29936467
## MADRS_Score_BL 0.324974211 0.151647841 2.1429531 0.03586572
## NIC_nM_BL 0.001219065 0.002085866 0.5844405 0.56094709
##
## $NIC_nM_LLOD_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.996246422 5.692243274 -0.5263736 0.60042026
## age 0.030144506 0.066097190 0.4560633 0.64986456
## sexfemale 1.865122863 1.782838848 1.0461534 0.29936504
## MADRS_Score_BL 0.324974059 0.151647861 2.1429518 0.03586583
## NIC_nM_LLOD_BL 0.001219043 0.002085868 0.5844296 0.56095435
##
## $NTA_nM_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.662755308 6.365279199 -0.7325296 0.4664787
## age 0.039120683 0.067415775 0.5802897 0.5637256
## sexfemale 2.150896705 1.792019255 1.2002643 0.2343930
## MADRS_Score_BL 0.325129700 0.151326538 2.1485306 0.0354024
## NTA_nM_BL 0.003376117 0.005045707 0.6691068 0.5057970
##
## $SAA_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.7783468351 5.6773446094 -0.4893744 0.62622413
## age 0.0248387002 0.0668905969 0.3713332 0.71159701
## sexfemale 1.8048660835 1.8032200084 1.0009129 0.32058059
## MADRS_Score_BL 0.3073054741 0.1515338590 2.0279657 0.04666631
## SAA_ng_mL_BL 0.0002081363 0.0003998231 0.5205710 0.60443446
##
## $VCAM_1_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.969674261 8.16123638 -0.1188146 0.90578866
## age 0.026473244 0.06729958 0.3933642 0.69533785
## sexfemale 1.856768158 1.81798999 1.0213302 0.31088431
## MADRS_Score_BL 0.307310789 0.15322713 2.0055899 0.04906627
## VCAM_1_ng_mL_BL -0.004163622 0.01388593 -0.2998447 0.76525243
##
## $ICAM_1_ng_mL_BL
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.10066886 6.08480841 -0.8382629 0.40495512
## age 0.02517098 0.06589206 0.3820032 0.70370509
## sexfemale 2.28783944 1.78928647 1.2786323 0.20557313
## MADRS_Score_BL 0.28120026 0.15335482 1.8336578 0.07128362
## ICAM_1_ng_mL_BL 0.01066193 0.01023648 1.0415619 0.3014734612 Model 4: Bayesian models
model1 <- brms::brm(MADRS_Score_3rd~age+sex+MADRS_Score_BL+TRP_nM_BL+five_HT_nM_BL+AA_nM_BL+IL2_pg_mL_BL, data=Biok_wide_new, family = "gaussian", prior = c(brms::set_prior("horseshoe(1)")))## Warning: Rows containing NAs were excluded from the model.## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start sampling##
## SAMPLING FOR MODEL '52c8d97328160311a61f9b26e277b9f1' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 5.1e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 1:
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## Chain 1: 0.661098 seconds (Sampling)
## Chain 1: 1.47035 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL '52c8d97328160311a61f9b26e277b9f1' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 2e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
## Chain 2: Adjust your expectations accordingly!
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## Chain 2: Elapsed Time: 1.15577 seconds (Warm-up)
## Chain 2: 0.553888 seconds (Sampling)
## Chain 2: 1.70966 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL '52c8d97328160311a61f9b26e277b9f1' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 5.5e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
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## Chain 3:
## Chain 3: Elapsed Time: 1.02127 seconds (Warm-up)
## Chain 3: 0.568625 seconds (Sampling)
## Chain 3: 1.5899 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL '52c8d97328160311a61f9b26e277b9f1' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 2e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
## Chain 4: Adjust your expectations accordingly!
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## Chain 4:## Warning: There were 486 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-essmodel1## Warning: There were 486 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See http://mc-stan.org/misc/
## warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: MADRS_Score_3rd ~ age + sex + MADRS_Score_BL + TRP_nM_BL + five_HT_nM_BL + AA_nM_BL + IL2_pg_mL_BL
## Data: Biok_wide_new (Number of observations: 44)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.18 5.28 -12.92 7.78 1.01 1393 1372
## age 0.01 0.03 -0.02 0.12 1.00 660 2177
## sexfemale -0.00 0.14 -0.17 0.15 1.01 725 1661
## MADRS_Score_BL 0.03 0.07 -0.03 0.26 1.00 475 1169
## TRP_nM_BL 0.00 0.00 0.00 0.00 1.01 1822 525
## five_HT_nM_BL 0.00 0.00 -0.00 0.01 1.01 797 2027
## AA_nM_BL -0.03 0.09 -0.36 0.04 1.00 723 1245
## IL2_pg_mL_BL 0.01 0.05 -0.04 0.16 1.00 1428 1841
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 5.83 0.62 4.81 7.22 1.00 1585 1564
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).model2 <- brms::brm(MADRS_Score_3rd~age+sex+MADRS_Score_BL, data=Biok_wide_new, family = "gaussian", prior = c(brms::set_prior("horseshoe(1)")))## Warning: Rows containing NAs were excluded from the model.## Compiling Stan program...
## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start sampling##
## SAMPLING FOR MODEL 'ab9e8ab03165e24c9459e4812c601265' NOW (CHAIN 1).
## Chain 1:
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##
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##
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##
## SAMPLING FOR MODEL 'ab9e8ab03165e24c9459e4812c601265' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 1.7e-05 seconds
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## Chain 4:## Warning: There were 255 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problemsmodel2## Warning: There were 255 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See http://mc-stan.org/misc/
## warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: MADRS_Score_3rd ~ age + sex + MADRS_Score_BL
## Data: Biok_wide_new (Number of observations: 70)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.28 5.71 -11.12 10.41 1.00 1733 2245
## age 0.01 0.06 -0.09 0.13 1.00 2711 2929
## sexfemale 0.57 1.04 -1.00 3.04 1.00 832 706
## MADRS_Score_BL 0.26 0.16 -0.02 0.59 1.00 1731 1533
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 6.98 0.59 6.00 8.20 1.01 696 2600
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).This will fit two Bayesian models to predict MADRS_Score_3rd using all the markers or none of the markers. Now we’ll begin to quantify how accurate we can be by comparing the two model’s accuracy. When this is fit, we can discuss how to turn it into something we can more easily interpret.