SII_SIRI_publication_output
Stephen Murata
05/08/2023
1 Raw distributions: Demographics/CBC
2 Log distributions: Demo/CBC variables
SG_raw_transformed<-SG_raw %>% select(-Arm, -Sex, -Cohort, -Subject_ID,-Group,-Remission, -Response )
SG_raw_transformed$MONO_BL<-as.numeric(SG_raw_transformed$MONO_BL)
vars_transformed<-c(names(SG_raw_transformed))
#natural log transform
SG_raw_transformed[vars_transformed]<-log(SG_raw_transformed[vars_transformed])
SG_raw_transformed %>% purrr::keep(is.numeric) %>% tidyr::gather() %>% ggplot(aes(value)) + facet_wrap(~ key, scales = "free") + geom_density() 
Take home: log transform looks better except for age, HAMD17_BL, HAMD17_WK8
3 KP/Cytokine inspection (RAW)
SMRI_Data_031317 <- read_excel("~/Desktop/SG_SII_SIRI/SMRI_Data_031317.xlsx")
biomarkers_all<-SMRI_Data_031317 %>% dplyr::select(-c(1:15)) %>% dplyr::select(-contains("WK4"))
# Distribution of kynurenines (baseline)
library(gtsummary)
biomarker_KP_BL<-biomarkers_all %>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(contains("KP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_KP_BL)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of kynurenines (WK8)
biomarker_KP_WK8<-biomarkers_all %>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("KP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_KP_WK8)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of growth factors (BL)
biomarker_GF_BL<-biomarkers_all %>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(contains("GF")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_GF_BL)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of growth factors (WK8)
biomarker_GF_WK8<-biomarkers_all%>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("GF")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_GF_WK8)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of cytokines (BL)
biomarker_CYTOKINES_BL<-biomarkers_all %>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(-contains("Week8")) %>%
dplyr::select(contains("IL"), contains("TNF"), contains("IFN"), contains("CRP"), contains("MCP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_CYTOKINES_BL)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distributions of cytokines (WK8)
biomarker_CYTOKINES_WK8<-biomarkers_all%>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("IL"), contains("TNF"), contains("IFN"), contains("CRP"), contains("MCP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_CYTOKINES_WK8)) %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# KP/Cytokines (log transformations)
# Distribution of kynurenines (baseline)
library(gtsummary)
biomarker_KP_BL<-biomarkers_all %>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(contains("KP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_KP_BL)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of kynurenines (WK8)
biomarker_KP_WK8<-biomarkers_all%>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("KP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_KP_WK8)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of growth factors (BL)
biomarker_GF_BL<-biomarkers_all %>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(contains("GF")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_GF_BL)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of growth factors (WK8)
biomarker_GF_WK8<-biomarkers_all %>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("GF")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_GF_WK8)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distribution of cytokines (BL)
biomarker_CYTOKINES_BL<-biomarkers_all%>%
dplyr::select(-contains("WK8")) %>%
dplyr::select(-contains("Week8")) %>%
dplyr::select(contains("IL"), contains("TNF"), contains("IFN"), contains("CRP"), contains("MCP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_CYTOKINES_BL)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
# Distributions of cytokines (WK8)
biomarker_CYTOKINES_WK8<-biomarkers_all %>%
dplyr::select(-contains("BL")) %>%
dplyr::select(contains("IL"), contains("TNF"), contains("IFN"), contains("CRP"), contains("MCP")) %>%
names()
SMRI_Data_031317 %>%
select(all_of(biomarker_CYTOKINES_WK8)) %>%
log() %>%
tidyr::gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_density() 
NOTE: SQRT transformations checked, not as good as log
4 Merging with log-transformed
#making df of KP/inflammatory log transformed markers
SMRI_Data_031317 <- read_excel("~/Desktop/SG_SII_SIRI/SMRI_Data_031317.xlsx")
biomarkers_all<-SMRI_Data_031317 %>%
dplyr::select(-c(1:15)) %>%
dplyr::select(-contains("WK4")) %>%
names()
DF1<-SMRI_Data_031317[,biomarkers_all]+2
DF1_log<-mutate_at(DF1, setNames(biomarkers_all, paste0("log_", biomarkers_all)), log) %>% select(contains("log"))
DF2<-SMRI_Data_031317 %>% select(Subject_ID)
Newdf<-cbind(DF2, DF1_log)
#making df of CBC/demo transformed markers
SG_bx_transformed<-SG_raw %>% select(-Arm, -Sex, -Age, -Subject_ID,-Group,-Remission, -Response, -Cohort, -HAMD17_BL,-HAMD17_WK8 ) %>% names()
SG_raw_log<-mutate_at(SG_raw, setNames(SG_bx_transformed, paste0("log_", SG_bx_transformed)), log) %>% dplyr::select(Subject_ID,Sex, Age, Group,Arm, Cohort, HAMD17_BL,HAMD17_WK8,Remission,Response,contains("log"))
#Merging
combined_df<-merge(SG_raw_log, Newdf, by="Subject_ID", all.x=TRUE, all.y=TRUE) 5 TABLE 1
combined_df$Cohort<-factor(combined_df$Cohort, levels=c("HC", "PBO_ESC", "CBX_ESC"))
table1<-createTable(compareGroups(Cohort~. -Subject_ID -Group -Arm, data=combined_df))6 Clinical outcome inspection
SG_df_new_long <- read_excel("~/Desktop/SG_SII_SIRI/SG_df_new_long_05082023.xlsx")
SG_df_new_long <- SG_df_new_long %>% subset(Pt_Group=="TRBDD")
SG_df_new_long$Pt_Group<-as.factor(SG_df_new_long$Pt_Group)
SG_df_new_long$Treatment<-as.factor(SG_df_new_long$Treatment)
SG_df_new_long$Sex<-as.factor(SG_df_new_long$Sex)
SG_df_new_long$Timepoint<-as.factor(SG_df_new_long$Timepoint)
ggplot(SG_df_new_long, aes(x = Timepoint, y = log(HAMD17_total)))+
geom_boxplot(aes(fill=Timepoint))+
geom_jitter(width = 0.1)+
facet_wrap(~Treatment)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=4)
7 Group comparison by remission status
byremission<-createTable(compareGroups(Remission~.-Subject_ID-Cohort-HAMD17_WK8-Response, data=combined_df))
strataTable( byremission, "Arm")##
## --------Summary descriptives table ---------
##
## _____________________________________________________________________________________________
## PBO_ESC CBX_ESC
## ________________________________ ___________________________________
## Non-remitter Remitter p.overall Non-remitter Remitter p.overall
## N=19 N=1 N=14 N=13
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Sex: 1.000 0.322
## Male 12 (63.2%) 1 (100%) 6 (42.9%) 9 (69.2%)
## Female 7 (36.8%) 0 (0.00%) 8 (57.1%) 4 (30.8%)
## Age 46.9 (13.4) 32.0 (.) . 37.4 (9.83) 41.7 (11.6) 0.315
## Group: . .
## HC 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
## PT 19 (100%) 1 (100%) 14 (100%) 13 (100%)
## Arm: . .
## PBO_ESC 19 (100%) 1 (100%) 0 (0.00%) 0 (0.00%)
## CBX_ESC 0 (0.00%) 0 (0.00%) 14 (100%) 13 (100%)
## HAMD17_BL 23.6 (6.38) 21.0 (.) . 27.6 (6.12) 22.2 (4.79) 0.017
## log_BMI 3.49 (0.18) 3.39 (.) . 3.44 (0.17) 3.38 (0.27) 0.495
## log_PLT_BL 5.44 (0.29) . (.) . 5.33 (0.25) 5.44 (0.22) 0.254
## log_MONO_BL -0.69 (0.41) . (.) . -0.79 (0.27) -0.75 (0.34) 0.772
## log_NEUT_BL 1.39 (0.35) . (.) . 1.23 (0.33) 1.29 (0.43) 0.658
## log_LYMPH_BL 0.62 (0.30) . (.) . 0.67 (0.30) 0.64 (0.27) 0.757
## log_PLT_WK8 5.33 (0.35) 5.36 (.) . 5.34 (0.21) 5.42 (0.22) 0.345
## log_MONO_WK8 -0.65 (0.39) -1.20 (.) . -0.76 (0.39) -0.85 (0.41) 0.586
## log_NEUT_WK8 1.43 (0.44) 1.13 (.) . 1.31 (0.42) 1.23 (0.34) 0.611
## log_LYMPH_WK8 0.64 (0.31) 0.00 (.) . 0.75 (0.37) 0.58 (0.35) 0.261
## log_SII_BL 6.19 (0.45) . (.) . 5.89 (0.40) 6.09 (0.63) 0.355
## log_SII_WK8 6.08 (0.52) . (.) . 5.81 (0.34) 6.08 (0.57) 0.172
## log_SIRI_BL 0.07 (0.59) . (.) . -0.23 (0.40) -0.09 (0.78) 0.585
## log_SIRI_WK8 0.09 (0.54) . (.) . -0.30 (0.40) -0.21 (0.72) 0.710
## log_IL1A_BL 0.75 (0.06) . (.) . 0.74 (0.08) 0.71 (0.04) 0.445
## log_IL1A_WK8 0.72 (0.05) . (.) . 0.69 (0.00) 0.72 (0.05) 0.173
## log_IL1B_BL 0.82 (0.16) . (.) . 0.72 (0.09) 0.83 (0.22) 0.311
## log_IL1B_WK8 0.75 (0.12) . (.) . 0.78 (0.14) 0.84 (0.20) 0.556
## log_IL2_BL 0.89 (0.53) . (.) . 0.69 (0.00) 0.91 (0.53) 0.363
## log_IL2_WK8 0.69 (0.00) . (.) . 0.97 (0.67) 0.87 (0.47) 0.778
## log_IL6_BL 1.39 (0.38) . (.) . 1.36 (0.42) 1.07 (0.24) 0.154
## log_IL6_WK8 1.21 (0.22) . (.) . 1.31 (0.33) 1.24 (0.23) 0.687
## log_IL8_BL 1.64 (0.59) . (.) . 1.62 (0.54) 1.47 (0.73) 0.678
## log_IL8_WK8 1.89 (0.99) . (.) . 1.23 (0.26) 1.78 (0.89) 0.156
## log_IFNG_BL 0.85 (0.27) . (.) . 1.01 (0.44) 0.69 (0.00) 0.102
## log_IFNG_WK8 0.69 (0.00) . (.) . 0.77 (0.18) 0.84 (0.25) 0.557
## log_TNFA_BL 1.51 (0.92) . (.) . 1.57 (0.91) 1.20 (0.30) 0.340
## log_TNFA_WK8 2.04 (1.37) . (.) . 1.23 (0.19) 1.49 (0.53) 0.269
## log_MCP1_BL 4.69 (0.38) . (.) . 4.68 (0.42) 4.50 (0.18) 0.345
## log_MCP1_WK8 4.39 (0.71) . (.) . 4.44 (0.68) 4.68 (0.47) 0.485
## log_CRPSet1_ug_ml_BL 1.37 (0.38) . (.) . 1.81 (0.78) 1.88 (1.00) 0.915
## log_CRPSet1_ug_ml_WK8 0.97 (0.17) . (.) . 1.78 (0.73) 1.35 (0.85) 0.420
## log_CRPSet2_µg_ml_BL 1.40 (0.71) . (.) . 1.62 (0.83) 1.21 (0.68) 0.230
## log_CRPSet2_µg_ml_WK8 2.06 (0.72) . (.) . 1.19 (0.63) 1.35 (0.70) 0.591
## log_IL1ARaox_7_16_BL 0.86 (0.07) . (.) . 0.83 (0.04) 0.85 (0.02) 0.144
## log_IL1ARaox_7_16_WK8 0.83 (0.03) . (.) . 0.81 (0.02) 0.83 (0.03) 0.200
## log_IL1BRaox_7_16_BL 1.15 (0.14) . (.) . 1.08 (0.12) 1.13 (0.15) 0.389
## log_IL1BRaox_7_16_WK8 1.16 (0.13) . (.) . 1.05 (0.06) 1.08 (0.12) 0.407
## log_IL2-Raox_7_16_BL 1.47 (0.13) . (.) . 1.45 (0.10) 1.59 (0.19) 0.088
## log_IL2-Raox_7_16_WK8 1.48 (0.15) . (.) . 1.41 (0.12) 1.48 (0.20) 0.363
## log_IL6Raox_7_16_BL 1.17 (0.21) . (.) . 1.24 (0.31) 1.13 (0.15) 0.279
## log_IL6Raox_7_16_WK8 1.12 (0.21) . (.) . 1.20 (0.23) 1.15 (0.19) 0.633
## log_IL8Raox_7_16_BL 1.87 (1.07) . (.) . 1.98 (1.02) 1.81 (0.73) 0.668
## log_IL8Raox_7_16_WK8 1.76 (0.69) . (.) . 1.85 (1.23) 1.80 (0.72) 0.916
## log_IFNGRaox_7_16_BL 0.94 (0.16) . (.) . 0.89 (0.09) 0.87 (0.08) 0.563
## log_IFNGRaox_7_16_Week8 0.88 (0.05) . (.) . 0.87 (0.10) 0.88 (0.11) 0.842
## log_TNFARaox_7_16_BL 1.94 (1.56) . (.) . 2.11 (1.55) 1.96 (1.18) 0.801
## log_TNFARaox_7_16_WK8 2.12 (1.46) . (.) . 1.78 (1.50) 1.78 (1.08) 0.992
## log_MCP1Raox_7_16_BL 4.46 (0.47) . (.) . 4.42 (0.72) 4.51 (0.36) 0.740
## log_MCP1Raox_7_16_WK8 4.44 (0.56) . (.) . 4.31 (0.81) 4.61 (1.14) 0.499
## log_IL4_BL 1.10 (0.41) . (.) . 1.22 (0.25) 1.09 (0.28) 0.356
## log_IL4_WK8 1.11 (0.29) . (.) . 0.86 (0.26) 1.07 (0.26) 0.169
## log_IL10_BL 0.95 (0.25) . (.) . 0.82 (0.22) 0.89 (0.22) 0.552
## log_IL10_WK8 0.82 (0.25) . (.) . 1.02 (0.37) 0.98 (0.19) 0.795
## log_IL4Raox_7_16_BL 1.51 (0.10) . (.) . 1.49 (0.15) 1.62 (0.09) 0.029
## log_IL4Raox_7_16_WK8 1.54 (0.13) . (.) . 1.48 (0.17) 1.54 (0.19) 0.453
## log_IL10Raox_7_16_BL 1.13 (0.35) . (.) . 1.06 (0.12) 1.10 (0.12) 0.449
## log_IL10Raox_7_16_WK8 1.07 (0.09) . (.) . 1.04 (0.09) 1.07 (0.09) 0.460
## log_FGF_BL 1.40 (0.29) . (.) . 1.05 (0.67) 1.35 (0.28) 0.453
## log_FGF_WK8 0.69 (.) . (.) . 1.29 (0.73) 1.21 (0.15) 0.813
## log_VEGF-Elisa_BL 3.47 (0.28) . (.) . 3.81 (0.42) 3.55 (0.31) 0.122
## log_VEGF-Elisa_WK8 3.66 (0.46) . (.) . 3.81 (0.41) 3.55 (0.40) 0.156
## log_VEGFRaoxold_BL 3.40 (0.65) . (.) . 3.34 (0.34) 3.20 (0.26) 0.429
## log_VEGFRaoxold_WK8 3.28 (0.71) . (.) . 3.18 (0.48) 3.23 (0.38) 0.814
## log_VEGFRaoxnew_BL 2.71 (0.55) . (.) . 2.79 (0.57) 2.74 (0.32) 0.802
## log_VEGFRaoxnew_WK8 2.68 (0.56) . (.) . 2.71 (0.57) 2.67 (0.35) 0.841
## log_EGF_BL 1.26 (0.42) . (.) . 1.24 (0.49) 1.16 (0.53) 0.790
## log_EGF_WK8 1.32 (0.20) . (.) . 1.24 (0.44) 1.51 (0.55) 0.339
## log_EGFRaox_7_16_BL 1.82 (0.43) . (.) . 1.56 (0.23) 1.83 (0.31) 0.044
## log_EGFRaox_7_16_WK8 1.69 (0.29) . (.) . 1.50 (0.20) 1.75 (0.40) 0.094
## log_KP_AA_BL 1.75 (0.42) 1.53 (.) . 1.49 (0.30) 1.64 (0.26) 0.216
## log_KP_AA_WK8 1.81 (0.48) 1.39 (.) . 1.69 (0.59) 1.61 (0.21) 0.644
## log_KP_KynA_BL 2.24 (0.28) 2.21 (.) . 2.28 (0.42) 2.26 (0.28) 0.873
## log_KP_KynA_WK8 2.13 (0.29) 1.99 (.) . 2.28 (0.35) 2.22 (0.38) 0.648
## log_KP_Trp_BL 9.60 (0.20) 9.57 (.) . 9.65 (0.21) 9.72 (0.28) 0.482
## log_KP_Trp_WK8 9.55 (0.21) 9.85 (.) . 9.67 (0.26) 9.61 (0.24) 0.548
## log_KP_Kyn_BL 5.84 (0.34) 5.65 (.) . 5.65 (0.31) 5.85 (0.32) 0.131
## log_KP_Kyn_WK8 5.79 (0.25) 5.68 (.) . 5.67 (0.33) 5.74 (0.38) 0.605
## log_KP_Xan_BL 1.53 (0.25) 1.82 (.) . 1.65 (0.45) 1.56 (0.24) 0.543
## log_KP_Xan_WK8 1.42 (0.24) 1.63 (.) . 1.66 (0.40) 1.73 (0.36) 0.695
## log_KP_Pic_BL 3.12 (0.47) 1.39 (.) . 3.04 (0.39) 3.11 (0.55) 0.697
## log_KP_Pic_WK8 3.08 (0.68) 1.55 (.) . 3.13 (0.48) 3.06 (0.63) 0.755
## log_KP_Quin_BL 4.20 (0.50) 3.61 (.) . 3.98 (0.39) 4.00 (0.41) 0.904
## log_KP_Quin_WK8 4.09 (0.48) 3.52 (.) . 4.04 (0.42) 3.94 (0.31) 0.474
## log_KP_QuinaldA_BL 1.26 (0.37) 1.34 (.) . 1.37 (0.29) 1.37 (0.20) 0.989
## log_KP_QuinaldA_WK8 1.23 (0.28) 1.39 (.) . 1.35 (0.27) 1.40 (0.26) 0.612
## log_KP_3HK_BL 2.90 (0.52) 3.28 (.) . 2.82 (0.68) 2.82 (0.51) 0.993
## log_KP_3HK_WK8 2.79 (0.51) 3.19 (.) . 2.74 (0.50) 2.80 (0.50) 0.759
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯8 Pearson heatmap: TRBDD patients at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_bl<-heatmap_df %>% select(-contains("WK8"), -contains("Week8")) %>% select(-log_IL2_BL) %>% tidyr::drop_na()
heatmap_bl<-as.matrix(sapply(heatmap_bl, as.numeric)) #IL2BL was problematic so removed
corr <- round(cor(heatmap_bl, use="pairwise.complete.obs"), 2)
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE, lab_size=1.5, insig="blank", tl.cex=5, title = "Pearson Correlation Matrix (TRBDD cohort at baseline)")
9 Pearson heatmap: TRBDD patients at week 8
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_wk8<-heatmap_df %>% select(-contains("BL")) %>% tidyr::drop_na()
heatmap_wk8<-as.matrix(sapply(heatmap_wk8, as.numeric))
corr <- round(cor(heatmap_wk8, use="pairwise.complete.obs"), 2)
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE, lab_size=1.5, insig="blank", tl.cex=5, title = "Pearson Correlation Matrix (TRBDD cohort at Week 8)")
10 Pearson heatmap: Non-remitters at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df_remitters <- heatmap_df[which(heatmap_df$Remission=="Non-remitter"), ]
heatmap_df_remitters_bl<-heatmap_df_remitters %>% select(contains("BL")) %>% select(-log_IL1A_BL,-log_IL2_BL,-log_IFNG_BL) %>% tidyr::drop_na()
heatmap_df_remitters_bl<-as.matrix(sapply(heatmap_df_remitters_bl, as.numeric))
corr <- round(cor(heatmap_df_remitters_bl, use="pairwise.complete.obs"), 2)
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE, lab_size=1.5, insig="blank", tl.cex=5, title = "Pearson Correlation Matrix (Non-remitters at Baseline)")
11 Pearson heatmap: Remitters at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df_remitters <- heatmap_df[which(heatmap_df$Remission=="Remitter"), ]
heatmap_df_remitters_bl<-heatmap_df_remitters %>% select(contains("BL")) %>% select(-log_IL1A_BL,-log_IL2_BL,-log_IFNG_BL) %>% tidyr::drop_na()
heatmap_df_remitters_bl<-as.matrix(sapply(heatmap_df_remitters_bl, as.numeric))
corr <- round(cor(heatmap_df_remitters_bl, use="pairwise.complete.obs"), 2)
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE, lab_size=1.5, insig="blank", tl.cex=5, title = "Pearson Correlation Matrix (Remitters at Baseline)")
12 Univariate screen by SII_BL
list_vars<-combined_df %>% dplyr::select(-Sex, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df <- heatmap_df[which(heatmap_df$Arm!=c("HC")), ] %>% select(-Arm)
gtsummary::tbl_uvregression(
heatmap_df,
lm,
log_SII_BL,
conf.int=TRUE
)| Characteristic | N | Beta | 95% CI1 | p-value |
|---|---|---|---|---|
| Age | 48 | -0.01 | -0.02, 0.01 | 0.3 |
| HAMD17_BL | 43 | -0.01 | -0.03, 0.02 | 0.6 |
| HAMD17_WK8 | 43 | 0.00 | -0.02, 0.02 | 0.8 |
| Remission | 43 | |||
| Non-remitter | — | — | ||
| Remitter | 0.03 | -0.31, 0.38 | 0.8 | |
| log_BMI | 47 | -0.17 | -1.0, 0.66 | 0.7 |
| log_PLT_BL | 50 | 0.90 | 0.41, 1.4 | <0.001 |
| log_MONO_BL | 50 | 0.32 | -0.08, 0.72 | 0.11 |
| log_NEUT_BL | 50 | 1.0 | 0.79, 1.3 | <0.001 |
| log_LYMPH_BL | 50 | -0.60 | -1.1, -0.10 | 0.020 |
| log_PLT_WK8 | 49 | 0.51 | -0.05, 1.1 | 0.073 |
| log_MONO_WK8 | 48 | 0.28 | -0.09, 0.65 | 0.13 |
| log_NEUT_WK8 | 48 | 0.57 | 0.27, 0.87 | <0.001 |
| log_LYMPH_WK8 | 48 | -0.13 | -0.61, 0.34 | 0.6 |
| log_SII_WK8 | 48 | 0.91 | 0.73, 1.1 | <0.001 |
| log_SIRI_BL | 50 | 0.63 | 0.47, 0.78 | <0.001 |
| log_SIRI_WK8 | 48 | 0.65 | 0.46, 0.83 | <0.001 |
| log_IL1A_BL | 21 | -1.0 | -4.4, 2.5 | 0.6 |
| log_IL1A_WK8 | 18 | 1.7 | -4.7, 8.1 | 0.6 |
| log_IL1B_BL | 22 | -0.56 | -1.8, 0.73 | 0.4 |
| log_IL1B_WK8 | 18 | 0.58 | -1.0, 2.2 | 0.4 |
| log_IL2_BL | 21 | 0.10 | -0.41, 0.62 | 0.7 |
| log_IL2_WK8 | 18 | 0.02 | -0.53, 0.56 | >0.9 |
| log_IL6_BL | 21 | -0.19 | -0.75, 0.38 | 0.5 |
| log_IL6_WK8 | 20 | -0.08 | -1.0, 0.83 | 0.9 |
| log_IL8_BL | 21 | -0.06 | -0.43, 0.30 | 0.7 |
| log_IL8_WK8 | 18 | 0.01 | -0.33, 0.35 | >0.9 |
| log_IFNG_BL | 21 | -0.54 | -1.2, 0.09 | 0.089 |
| log_IFNG_WK8 | 18 | 0.03 | -1.3, 1.4 | >0.9 |
| log_TNFA_BL | 21 | -0.08 | -0.36, 0.21 | 0.6 |
| log_TNFA_WK8 | 18 | -0.07 | -0.42, 0.27 | 0.7 |
| log_MCP1_BL | 21 | -0.39 | -1.0, 0.22 | 0.2 |
| log_MCP1_WK8 | 18 | -0.11 | -0.55, 0.34 | 0.6 |
| log_CRPSet1_ug_ml_BL | 15 | 0.14 | -0.28, 0.56 | 0.5 |
| log_CRPSet1_ug_ml_WK8 | 13 | 0.14 | -0.28, 0.55 | 0.5 |
| log_CRPSet2_µg_ml_BL | 32 | 0.12 | -0.14, 0.38 | 0.4 |
| log_CRPSet2_µg_ml_WK8 | 31 | 0.24 | 0.00, 0.48 | 0.048 |
| log_IL1ARaox_7_16_BL | 30 | 1.1 | -3.0, 5.2 | 0.6 |
| log_IL1ARaox_7_16_WK8 | 30 | 0.50 | -6.5, 7.5 | 0.9 |
| log_IL1BRaox_7_16_BL | 30 | 0.51 | -1.0, 2.0 | 0.5 |
| log_IL1BRaox_7_16_WK8 | 30 | 1.8 | 0.19, 3.5 | 0.030 |
| log_IL2-Raox_7_16_BL | 25 | 1.0 | -0.24, 2.3 | 0.11 |
| log_IL2-Raox_7_16_WK8 | 30 | 0.35 | -0.89, 1.6 | 0.6 |
| log_IL6Raox_7_16_BL | 30 | -0.16 | -1.0, 0.69 | 0.7 |
| log_IL6Raox_7_16_WK8 | 31 | 0.09 | -0.86, 1.0 | 0.8 |
| log_IL8Raox_7_16_BL | 30 | -0.13 | -0.35, 0.08 | 0.2 |
| log_IL8Raox_7_16_WK8 | 30 | 0.03 | -0.19, 0.25 | 0.8 |
| log_IFNGRaox_7_16_BL | 30 | 0.36 | -1.4, 2.1 | 0.7 |
| log_IFNGRaox_7_16_Week8 | 30 | 1.0 | -1.3, 3.2 | 0.4 |
| log_TNFARaox_7_16_BL | 30 | -0.09 | -0.23, 0.05 | 0.2 |
| log_TNFARaox_7_16_WK8 | 30 | 0.04 | -0.11, 0.19 | 0.6 |
| log_MCP1Raox_7_16_BL | 30 | -0.15 | -0.52, 0.23 | 0.4 |
| log_MCP1Raox_7_16_WK8 | 30 | -0.15 | -0.38, 0.07 | 0.2 |
| log_IL4_BL | 26 | -0.60 | -1.3, 0.07 | 0.075 |
| log_IL4_WK8 | 18 | 0.23 | -0.69, 1.2 | 0.6 |
| log_IL10_BL | 21 | 0.08 | -0.85, 1.0 | 0.9 |
| log_IL10_WK8 | 18 | -0.30 | -1.2, 0.63 | 0.5 |
| log_IL4Raox_7_16_BL | 30 | -0.51 | -2.1, 1.1 | 0.5 |
| log_IL4Raox_7_16_WK8 | 30 | -0.20 | -1.4, 1.0 | 0.7 |
| log_IL10Raox_7_16_BL | 30 | -0.67 | -1.6, 0.22 | 0.14 |
| log_IL10Raox_7_16_WK8 | 30 | -1.1 | -3.4, 1.1 | 0.3 |
| log_FGF_BL | 15 | 0.17 | -0.48, 0.83 | 0.6 |
| log_FGF_WK8 | 9 | -0.06 | -0.81, 0.69 | 0.9 |
| log_VEGF-Elisa_BL | 31 | -0.63 | -1.1, -0.16 | 0.011 |
| log_VEGF-Elisa_WK8 | 30 | -0.33 | -0.78, 0.13 | 0.2 |
| log_VEGFRaoxold_BL | 21 | -0.23 | -0.71, 0.24 | 0.3 |
| log_VEGFRaoxold_WK8 | 18 | -0.15 | -0.71, 0.40 | 0.6 |
| log_VEGFRaoxnew_BL | 30 | -0.26 | -0.67, 0.14 | 0.2 |
| log_VEGFRaoxnew_WK8 | 30 | -0.06 | -0.47, 0.35 | 0.8 |
| log_EGF_BL | 21 | -0.14 | -0.60, 0.31 | 0.5 |
| log_EGF_WK8 | 18 | 0.04 | -0.53, 0.60 | 0.9 |
| log_EGFRaox_7_16_BL | 30 | 0.38 | -0.19, 0.94 | 0.2 |
| log_EGFRaox_7_16_WK8 | 30 | 0.45 | -0.15, 1.0 | 0.14 |
| log_KP_AA_BL | 39 | 0.14 | -0.31, 0.58 | 0.5 |
| log_KP_AA_WK8 | 37 | -0.05 | -0.41, 0.30 | 0.8 |
| log_KP_KynA_BL | 39 | -0.27 | -0.75, 0.21 | 0.3 |
| log_KP_KynA_WK8 | 37 | -0.37 | -0.83, 0.10 | 0.12 |
| log_KP_Trp_BL | 39 | -0.30 | -1.0, 0.37 | 0.4 |
| log_KP_Trp_WK8 | 37 | -0.49 | -1.2, 0.18 | 0.14 |
| log_KP_Kyn_BL | 39 | 0.04 | -0.45, 0.52 | 0.9 |
| log_KP_Kyn_WK8 | 37 | -0.16 | -0.67, 0.36 | 0.5 |
| log_KP_Xan_BL | 38 | -0.23 | -0.73, 0.27 | 0.4 |
| log_KP_Xan_WK8 | 33 | -0.35 | -0.84, 0.14 | 0.2 |
| log_KP_Pic_BL | 39 | 0.06 | -0.28, 0.40 | 0.7 |
| log_KP_Pic_WK8 | 37 | 0.13 | -0.14, 0.40 | 0.3 |
| log_KP_Quin_BL | 39 | 0.13 | -0.22, 0.48 | 0.5 |
| log_KP_Quin_WK8 | 37 | 0.01 | -0.38, 0.41 | >0.9 |
| log_KP_QuinaldA_BL | 39 | -0.09 | -0.62, 0.43 | 0.7 |
| log_KP_QuinaldA_WK8 | 37 | 0.01 | -0.58, 0.61 | >0.9 |
| log_KP_3HK_BL | 39 | -0.24 | -0.50, 0.03 | 0.082 |
| log_KP_3HK_WK8 | 37 | -0.13 | -0.46, 0.20 | 0.4 |
| 1 CI = Confidence Interval | ||||
13 Univariate screen by NEUT_BL
list_vars<-combined_df %>% dplyr::select(-Sex, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df <- heatmap_df[which(heatmap_df$Arm!=c("HC")), ] %>% select(-Arm)
gtsummary::tbl_uvregression(
heatmap_df,
lm,
log_NEUT_BL,
conf.int=TRUE
)| Characteristic | N | Beta | 95% CI1 | p-value |
|---|---|---|---|---|
| Age | 48 | -0.01 | -0.02, 0.00 | 0.13 |
| HAMD17_BL | 43 | -0.01 | -0.03, 0.01 | 0.3 |
| HAMD17_WK8 | 43 | 0.01 | -0.01, 0.02 | 0.5 |
| Remission | 43 | |||
| Non-remitter | — | — | ||
| Remitter | -0.02 | -0.28, 0.23 | 0.9 | |
| log_BMI | 47 | -0.12 | -0.74, 0.49 | 0.7 |
| log_PLT_BL | 50 | 0.22 | -0.20, 0.64 | 0.3 |
| log_MONO_BL | 50 | 0.49 | 0.22, 0.76 | <0.001 |
| log_LYMPH_BL | 50 | 0.13 | -0.27, 0.52 | 0.5 |
| log_PLT_WK8 | 49 | 0.18 | -0.25, 0.60 | 0.4 |
| log_MONO_WK8 | 48 | 0.34 | 0.07, 0.60 | 0.014 |
| log_NEUT_WK8 | 48 | 0.58 | 0.39, 0.77 | <0.001 |
| log_LYMPH_WK8 | 48 | 0.28 | -0.06, 0.63 | 0.11 |
| log_SII_BL | 50 | 0.58 | 0.44, 0.72 | <0.001 |
| log_SII_WK8 | 48 | 0.59 | 0.42, 0.76 | <0.001 |
| log_SIRI_BL | 50 | 0.52 | 0.43, 0.62 | <0.001 |
| log_SIRI_WK8 | 48 | 0.52 | 0.40, 0.65 | <0.001 |
| log_IL1A_BL | 21 | -1.1 | -4.1, 1.8 | 0.4 |
| log_IL1A_WK8 | 18 | -0.32 | -6.0, 5.4 | >0.9 |
| log_IL1B_BL | 22 | -0.39 | -1.5, 0.69 | 0.5 |
| log_IL1B_WK8 | 18 | 0.01 | -1.4, 1.4 | >0.9 |
| log_IL2_BL | 21 | 0.04 | -0.41, 0.48 | 0.9 |
| log_IL2_WK8 | 18 | -0.07 | -0.55, 0.40 | 0.7 |
| log_IL6_BL | 21 | 0.01 | -0.47, 0.49 | >0.9 |
| log_IL6_WK8 | 20 | 0.12 | -0.68, 0.92 | 0.8 |
| log_IL8_BL | 21 | -0.01 | -0.32, 0.30 | >0.9 |
| log_IL8_WK8 | 18 | -0.04 | -0.34, 0.25 | 0.8 |
| log_IFNG_BL | 21 | 0.16 | -0.41, 0.73 | 0.6 |
| log_IFNG_WK8 | 18 | -0.23 | -1.4, 1.0 | 0.7 |
| log_TNFA_BL | 21 | 0.09 | -0.14, 0.33 | 0.4 |
| log_TNFA_WK8 | 18 | -0.05 | -0.35, 0.26 | 0.8 |
| log_MCP1_BL | 21 | -0.36 | -0.87, 0.15 | 0.2 |
| log_MCP1_WK8 | 18 | -0.05 | -0.45, 0.34 | 0.8 |
| log_CRPSet1_ug_ml_BL | 15 | 0.20 | -0.10, 0.51 | 0.2 |
| log_CRPSet1_ug_ml_WK8 | 13 | 0.13 | -0.16, 0.42 | 0.4 |
| log_CRPSet2_µg_ml_BL | 32 | 0.03 | -0.16, 0.23 | 0.7 |
| log_CRPSet2_µg_ml_WK8 | 31 | 0.03 | -0.16, 0.22 | 0.8 |
| log_IL1ARaox_7_16_BL | 30 | -0.21 | -3.3, 2.9 | 0.9 |
| log_IL1ARaox_7_16_WK8 | 30 | 0.19 | -5.0, 5.4 | >0.9 |
| log_IL1BRaox_7_16_BL | 30 | 0.65 | -0.46, 1.7 | 0.2 |
| log_IL1BRaox_7_16_WK8 | 30 | 1.1 | -0.15, 2.4 | 0.083 |
| log_IL2-Raox_7_16_BL | 25 | 0.75 | -0.35, 1.9 | 0.2 |
| log_IL2-Raox_7_16_WK8 | 30 | 0.45 | -0.46, 1.4 | 0.3 |
| log_IL6Raox_7_16_BL | 30 | -0.03 | -0.67, 0.61 | >0.9 |
| log_IL6Raox_7_16_WK8 | 31 | 0.21 | -0.48, 0.91 | 0.5 |
| log_IL8Raox_7_16_BL | 30 | -0.02 | -0.18, 0.14 | 0.8 |
| log_IL8Raox_7_16_WK8 | 30 | 0.03 | -0.13, 0.20 | 0.7 |
| log_IFNGRaox_7_16_BL | 30 | 0.72 | -0.54, 2.0 | 0.3 |
| log_IFNGRaox_7_16_Week8 | 30 | 0.46 | -1.2, 2.1 | 0.6 |
| log_TNFARaox_7_16_BL | 30 | 0.01 | -0.10, 0.12 | 0.8 |
| log_TNFARaox_7_16_WK8 | 30 | 0.04 | -0.07, 0.15 | 0.4 |
| log_MCP1Raox_7_16_BL | 30 | -0.10 | -0.38, 0.17 | 0.4 |
| log_MCP1Raox_7_16_WK8 | 30 | -0.10 | -0.26, 0.07 | 0.3 |
| log_IL4_BL | 26 | -0.35 | -0.81, 0.11 | 0.13 |
| log_IL4_WK8 | 18 | 0.35 | -0.45, 1.2 | 0.4 |
| log_IL10_BL | 21 | 0.10 | -0.69, 0.89 | 0.8 |
| log_IL10_WK8 | 18 | -0.14 | -1.0, 0.69 | 0.7 |
| log_IL4Raox_7_16_BL | 30 | -1.0 | -2.1, 0.17 | 0.091 |
| log_IL4Raox_7_16_WK8 | 30 | -0.34 | -1.2, 0.54 | 0.4 |
| log_IL10Raox_7_16_BL | 30 | -0.50 | -1.2, 0.17 | 0.14 |
| log_IL10Raox_7_16_WK8 | 30 | -1.6 | -3.2, -0.07 | 0.042 |
| log_FGF_BL | 15 | 0.01 | -0.50, 0.52 | >0.9 |
| log_FGF_WK8 | 9 | 0.20 | -0.29, 0.70 | 0.4 |
| log_VEGF-Elisa_BL | 31 | -0.10 | -0.49, 0.30 | 0.6 |
| log_VEGF-Elisa_WK8 | 30 | -0.03 | -0.38, 0.32 | 0.9 |
| log_VEGFRaoxold_BL | 21 | -0.02 | -0.43, 0.39 | >0.9 |
| log_VEGFRaoxold_WK8 | 18 | 0.03 | -0.47, 0.52 | >0.9 |
| log_VEGFRaoxnew_BL | 30 | -0.04 | -0.35, 0.27 | 0.8 |
| log_VEGFRaoxnew_WK8 | 30 | 0.04 | -0.26, 0.34 | 0.8 |
| log_EGF_BL | 21 | -0.12 | -0.50, 0.27 | 0.5 |
| log_EGF_WK8 | 18 | -0.07 | -0.57, 0.43 | 0.8 |
| log_EGFRaox_7_16_BL | 30 | -0.03 | -0.46, 0.40 | 0.9 |
| log_EGFRaox_7_16_WK8 | 30 | 0.02 | -0.44, 0.48 | >0.9 |
| log_KP_AA_BL | 39 | 0.08 | -0.26, 0.43 | 0.6 |
| log_KP_AA_WK8 | 37 | 0.00 | -0.27, 0.27 | >0.9 |
| log_KP_KynA_BL | 39 | -0.06 | -0.44, 0.32 | 0.7 |
| log_KP_KynA_WK8 | 37 | -0.19 | -0.56, 0.17 | 0.3 |
| log_KP_Trp_BL | 39 | 0.08 | -0.44, 0.60 | 0.7 |
| log_KP_Trp_WK8 | 37 | -0.26 | -0.78, 0.26 | 0.3 |
| log_KP_Kyn_BL | 39 | 0.30 | -0.07, 0.66 | 0.10 |
| log_KP_Kyn_WK8 | 37 | -0.04 | -0.43, 0.36 | 0.9 |
| log_KP_Xan_BL | 38 | -0.06 | -0.45, 0.33 | 0.8 |
| log_KP_Xan_WK8 | 33 | -0.31 | -0.66, 0.05 | 0.091 |
| log_KP_Pic_BL | 39 | 0.18 | -0.08, 0.44 | 0.2 |
| log_KP_Pic_WK8 | 37 | 0.18 | -0.02, 0.38 | 0.069 |
| log_KP_Quin_BL | 39 | 0.12 | -0.15, 0.38 | 0.4 |
| log_KP_Quin_WK8 | 37 | -0.03 | -0.33, 0.27 | 0.8 |
| log_KP_QuinaldA_BL | 39 | 0.18 | -0.22, 0.58 | 0.4 |
| log_KP_QuinaldA_WK8 | 37 | 0.15 | -0.31, 0.60 | 0.5 |
| log_KP_3HK_BL | 39 | -0.09 | -0.31, 0.12 | 0.4 |
| log_KP_3HK_WK8 | 37 | -0.06 | -0.32, 0.19 | 0.6 |
| 1 CI = Confidence Interval | ||||
14 Univariate screen by SIRI_BL
list_vars<-combined_df %>% dplyr::select(-Sex, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df <- heatmap_df[which(heatmap_df$Arm!=c("HC")), ] %>% select(-Arm)
gtsummary::tbl_uvregression(
heatmap_df,
lm,
log_SIRI_BL,
conf.int=TRUE
)| Characteristic | N | Beta | 95% CI1 | p-value |
|---|---|---|---|---|
| Age | 48 | -0.01 | -0.02, 0.01 | 0.2 |
| HAMD17_BL | 43 | -0.03 | -0.06, 0.00 | 0.055 |
| HAMD17_WK8 | 43 | 0.01 | -0.01, 0.03 | 0.4 |
| Remission | 43 | |||
| Non-remitter | — | — | ||
| Remitter | -0.03 | -0.44, 0.39 | >0.9 | |
| log_BMI | 47 | -0.55 | -1.5, 0.44 | 0.3 |
| log_PLT_BL | 50 | 0.16 | -0.52, 0.84 | 0.6 |
| log_MONO_BL | 50 | 1.2 | 0.83, 1.5 | <0.001 |
| log_NEUT_BL | 50 | 1.4 | 1.1, 1.6 | <0.001 |
| log_LYMPH_BL | 50 | -0.37 | -1.0, 0.27 | 0.3 |
| log_PLT_WK8 | 49 | 0.06 | -0.63, 0.75 | 0.9 |
| log_MONO_WK8 | 48 | 0.80 | 0.41, 1.2 | <0.001 |
| log_NEUT_WK8 | 48 | 0.77 | 0.42, 1.1 | <0.001 |
| log_LYMPH_WK8 | 48 | 0.10 | -0.47, 0.67 | 0.7 |
| log_SII_BL | 50 | 0.92 | 0.69, 1.1 | <0.001 |
| log_SII_WK8 | 48 | 0.88 | 0.59, 1.2 | <0.001 |
| log_SIRI_WK8 | 48 | 1.0 | 0.83, 1.1 | <0.001 |
| log_IL1A_BL | 21 | -2.6 | -6.4, 1.2 | 0.2 |
| log_IL1A_WK8 | 18 | -1.5 | -9.6, 6.6 | 0.7 |
| log_IL1B_BL | 22 | -0.80 | -2.3, 0.66 | 0.3 |
| log_IL1B_WK8 | 18 | -0.27 | -2.3, 1.8 | 0.8 |
| log_IL2_BL | 21 | 0.01 | -0.58, 0.61 | >0.9 |
| log_IL2_WK8 | 18 | -0.19 | -0.86, 0.49 | 0.6 |
| log_IL6_BL | 21 | -0.30 | -0.93, 0.33 | 0.3 |
| log_IL6_WK8 | 20 | -0.43 | -1.5, 0.69 | 0.4 |
| log_IL8_BL | 21 | -0.02 | -0.44, 0.40 | >0.9 |
| log_IL8_WK8 | 18 | 0.09 | -0.34, 0.51 | 0.7 |
| log_IFNG_BL | 21 | -0.01 | -0.79, 0.77 | >0.9 |
| log_IFNG_WK8 | 18 | -0.67 | -2.4, 1.0 | 0.4 |
| log_TNFA_BL | 21 | 0.06 | -0.27, 0.38 | 0.7 |
| log_TNFA_WK8 | 18 | 0.09 | -0.34, 0.53 | 0.7 |
| log_MCP1_BL | 21 | -0.92 | -1.5, -0.35 | 0.003 |
| log_MCP1_WK8 | 18 | -0.13 | -0.69, 0.43 | 0.6 |
| log_CRPSet1_ug_ml_BL | 15 | 0.15 | -0.35, 0.66 | 0.5 |
| log_CRPSet1_ug_ml_WK8 | 13 | 0.09 | -0.34, 0.52 | 0.7 |
| log_CRPSet2_µg_ml_BL | 32 | 0.08 | -0.23, 0.39 | 0.6 |
| log_CRPSet2_µg_ml_WK8 | 31 | 0.07 | -0.24, 0.38 | 0.7 |
| log_IL1ARaox_7_16_BL | 30 | 1.5 | -3.4, 6.5 | 0.5 |
| log_IL1ARaox_7_16_WK8 | 30 | -1.7 | -10, 6.8 | 0.7 |
| log_IL1BRaox_7_16_BL | 30 | 1.0 | -0.78, 2.8 | 0.3 |
| log_IL1BRaox_7_16_WK8 | 30 | 1.9 | -0.17, 4.0 | 0.070 |
| log_IL2-Raox_7_16_BL | 25 | 1.6 | 0.03, 3.2 | 0.046 |
| log_IL2-Raox_7_16_WK8 | 30 | 0.47 | -1.1, 2.0 | 0.5 |
| log_IL6Raox_7_16_BL | 30 | -0.17 | -1.2, 0.86 | 0.7 |
| log_IL6Raox_7_16_WK8 | 31 | -0.04 | -1.2, 1.1 | >0.9 |
| log_IL8Raox_7_16_BL | 30 | -0.09 | -0.35, 0.17 | 0.5 |
| log_IL8Raox_7_16_WK8 | 30 | 0.07 | -0.20, 0.34 | 0.6 |
| log_IFNGRaox_7_16_BL | 30 | 0.76 | -1.3, 2.8 | 0.5 |
| log_IFNGRaox_7_16_Week8 | 30 | 0.45 | -2.3, 3.2 | 0.7 |
| log_TNFARaox_7_16_BL | 30 | -0.02 | -0.19, 0.16 | 0.8 |
| log_TNFARaox_7_16_WK8 | 30 | 0.09 | -0.09, 0.27 | 0.3 |
| log_MCP1Raox_7_16_BL | 30 | -0.32 | -0.75, 0.12 | 0.15 |
| log_MCP1Raox_7_16_WK8 | 30 | -0.24 | -0.51, 0.03 | 0.075 |
| log_IL4_BL | 26 | -0.65 | -1.3, 0.04 | 0.063 |
| log_IL4_WK8 | 18 | 0.17 | -1.0, 1.3 | 0.8 |
| log_IL10_BL | 21 | 0.35 | -0.70, 1.4 | 0.5 |
| log_IL10_WK8 | 18 | -0.74 | -1.9, 0.38 | 0.2 |
| log_IL4Raox_7_16_BL | 30 | -1.5 | -3.3, 0.34 | 0.11 |
| log_IL4Raox_7_16_WK8 | 30 | -0.47 | -1.9, 1.0 | 0.5 |
| log_IL10Raox_7_16_BL | 30 | -0.60 | -1.7, 0.49 | 0.3 |
| log_IL10Raox_7_16_WK8 | 30 | -2.3 | -4.9, 0.37 | 0.090 |
| log_FGF_BL | 15 | 0.16 | -0.63, 0.95 | 0.7 |
| log_FGF_WK8 | 9 | 0.02 | -0.75, 0.80 | >0.9 |
| log_VEGF-Elisa_BL | 31 | -0.31 | -0.94, 0.32 | 0.3 |
| log_VEGF-Elisa_WK8 | 30 | -0.21 | -0.78, 0.36 | 0.4 |
| log_VEGFRaoxold_BL | 21 | -0.33 | -0.86, 0.21 | 0.2 |
| log_VEGFRaoxold_WK8 | 18 | -0.11 | -0.81, 0.59 | 0.7 |
| log_VEGFRaoxnew_BL | 30 | -0.24 | -0.73, 0.26 | 0.3 |
| log_VEGFRaoxnew_WK8 | 30 | -0.13 | -0.63, 0.37 | 0.6 |
| log_EGF_BL | 21 | -0.17 | -0.69, 0.35 | 0.5 |
| log_EGF_WK8 | 18 | -0.32 | -1.0, 0.37 | 0.3 |
| log_EGFRaox_7_16_BL | 30 | 0.02 | -0.67, 0.72 | >0.9 |
| log_EGFRaox_7_16_WK8 | 30 | 0.21 | -0.55, 1.0 | 0.6 |
| log_KP_AA_BL | 39 | 0.39 | -0.17, 1.0 | 0.2 |
| log_KP_AA_WK8 | 37 | 0.08 | -0.37, 0.53 | 0.7 |
| log_KP_KynA_BL | 39 | 0.06 | -0.57, 0.69 | 0.9 |
| log_KP_KynA_WK8 | 37 | -0.09 | -0.70, 0.51 | 0.8 |
| log_KP_Trp_BL | 39 | -0.11 | -1.0, 0.76 | 0.8 |
| log_KP_Trp_WK8 | 37 | -0.35 | -1.2, 0.51 | 0.4 |
| log_KP_Kyn_BL | 39 | 0.27 | -0.35, 0.90 | 0.4 |
| log_KP_Kyn_WK8 | 37 | -0.07 | -0.71, 0.58 | 0.8 |
| log_KP_Xan_BL | 38 | 0.11 | -0.54, 0.77 | 0.7 |
| log_KP_Xan_WK8 | 33 | -0.35 | -0.94, 0.25 | 0.2 |
| log_KP_Pic_BL | 39 | 0.26 | -0.18, 0.69 | 0.2 |
| log_KP_Pic_WK8 | 37 | 0.29 | -0.03, 0.62 | 0.078 |
| log_KP_Quin_BL | 39 | 0.32 | -0.12, 0.76 | 0.15 |
| log_KP_Quin_WK8 | 37 | 0.24 | -0.25, 0.73 | 0.3 |
| log_KP_QuinaldA_BL | 39 | 0.46 | -0.20, 1.1 | 0.2 |
| log_KP_QuinaldA_WK8 | 37 | 0.50 | -0.23, 1.2 | 0.2 |
| log_KP_3HK_BL | 39 | -0.24 | -0.60, 0.11 | 0.2 |
| log_KP_3HK_WK8 | 37 | -0.15 | -0.56, 0.27 | 0.5 |
| 1 CI = Confidence Interval | ||||
15 Univariate screen by MONO_BL
list_vars<-combined_df %>% dplyr::select(-Sex, -Response, -Subject_ID, -Group, -Cohort) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df <- heatmap_df[which(heatmap_df$Arm!=c("HC")), ] %>% select(-Arm)
gtsummary::tbl_uvregression(
heatmap_df,
lm,
log_MONO_BL,
conf.int=TRUE
)| Characteristic | N | Beta | 95% CI1 | p-value |
|---|---|---|---|---|
| Age | 49 | 0.00 | -0.01, 0.01 | 0.5 |
| HAMD17_BL | 43 | -0.02 | -0.03, 0.00 | 0.058 |
| HAMD17_WK8 | 43 | 0.01 | -0.01, 0.02 | 0.4 |
| Remission | 43 | |||
| Non-remitter | — | — | ||
| Remitter | -0.01 | -0.25, 0.22 | >0.9 | |
| log_BMI | 48 | -0.18 | -0.78, 0.42 | 0.6 |
| log_PLT_BL | 51 | 0.25 | -0.16, 0.66 | 0.2 |
| log_NEUT_BL | 50 | 0.44 | 0.20, 0.68 | <0.001 |
| log_LYMPH_BL | 51 | 0.43 | 0.06, 0.80 | 0.022 |
| log_PLT_WK8 | 50 | 0.12 | -0.30, 0.53 | 0.6 |
| log_MONO_WK8 | 49 | 0.74 | 0.58, 0.91 | <0.001 |
| log_NEUT_WK8 | 49 | 0.39 | 0.17, 0.61 | <0.001 |
| log_LYMPH_WK8 | 49 | 0.49 | 0.17, 0.81 | 0.003 |
| log_SII_BL | 50 | 0.16 | -0.04, 0.36 | 0.11 |
| log_SII_WK8 | 48 | 0.11 | -0.11, 0.34 | 0.3 |
| log_SIRI_BL | 50 | 0.40 | 0.28, 0.53 | <0.001 |
| log_SIRI_WK8 | 48 | 0.39 | 0.25, 0.54 | <0.001 |
| log_IL1A_BL | 21 | -1.8 | -4.1, 0.38 | 0.10 |
| log_IL1A_WK8 | 18 | -2.5 | -6.5, 1.5 | 0.2 |
| log_IL1B_BL | 22 | -0.17 | -1.1, 0.71 | 0.7 |
| log_IL1B_WK8 | 18 | -0.29 | -1.3, 0.76 | 0.6 |
| log_IL2_BL | 21 | 0.23 | -0.11, 0.57 | 0.2 |
| log_IL2_WK8 | 18 | -0.07 | -0.42, 0.29 | 0.7 |
| log_IL6_BL | 21 | 0.24 | -0.13, 0.61 | 0.2 |
| log_IL6_WK8 | 20 | -0.02 | -0.65, 0.61 | >0.9 |
| log_IL8_BL | 21 | 0.11 | -0.13, 0.35 | 0.4 |
| log_IL8_WK8 | 18 | 0.11 | -0.10, 0.32 | 0.3 |
| log_IFNG_BL | 21 | 0.22 | -0.24, 0.67 | 0.3 |
| log_IFNG_WK8 | 18 | -0.89 | -1.7, -0.12 | 0.026 |
| log_TNFA_BL | 21 | 0.16 | -0.01, 0.34 | 0.068 |
| log_TNFA_WK8 | 18 | 0.14 | -0.07, 0.36 | 0.2 |
| log_MCP1_BL | 21 | -0.31 | -0.71, 0.10 | 0.13 |
| log_MCP1_WK8 | 18 | -0.09 | -0.38, 0.20 | 0.5 |
| log_CRPSet1_ug_ml_BL | 15 | 0.15 | -0.11, 0.40 | 0.2 |
| log_CRPSet1_ug_ml_WK8 | 13 | 0.06 | -0.14, 0.27 | 0.5 |
| log_CRPSet2_µg_ml_BL | 32 | 0.09 | -0.06, 0.24 | 0.2 |
| log_CRPSet2_µg_ml_WK8 | 31 | -0.07 | -0.22, 0.08 | 0.4 |
| log_IL1ARaox_7_16_BL | 30 | 0.83 | -1.6, 3.3 | 0.5 |
| log_IL1ARaox_7_16_WK8 | 30 | 1.5 | -2.7, 5.7 | 0.5 |
| log_IL1BRaox_7_16_BL | 30 | 0.63 | -0.24, 1.5 | 0.15 |
| log_IL1BRaox_7_16_WK8 | 30 | 0.62 | -0.45, 1.7 | 0.2 |
| log_IL2-Raox_7_16_BL | 25 | 0.88 | 0.06, 1.7 | 0.036 |
| log_IL2-Raox_7_16_WK8 | 30 | 0.62 | -0.11, 1.3 | 0.092 |
| log_IL6Raox_7_16_BL | 30 | 0.43 | -0.05, 0.92 | 0.078 |
| log_IL6Raox_7_16_WK8 | 31 | 0.41 | -0.13, 1.0 | 0.13 |
| log_IL8Raox_7_16_BL | 30 | 0.09 | -0.03, 0.22 | 0.14 |
| log_IL8Raox_7_16_WK8 | 30 | 0.04 | -0.09, 0.18 | 0.5 |
| log_IFNGRaox_7_16_BL | 30 | 0.81 | -0.17, 1.8 | 0.10 |
| log_IFNGRaox_7_16_Week8 | 30 | -0.38 | -1.7, 1.0 | 0.6 |
| log_TNFARaox_7_16_BL | 30 | 0.09 | 0.01, 0.17 | 0.035 |
| log_TNFARaox_7_16_WK8 | 30 | 0.06 | -0.03, 0.15 | 0.2 |
| log_MCP1Raox_7_16_BL | 30 | -0.03 | -0.25, 0.20 | 0.8 |
| log_MCP1Raox_7_16_WK8 | 30 | -0.02 | -0.16, 0.12 | 0.7 |
| log_IL4_BL | 26 | 0.13 | -0.27, 0.52 | 0.5 |
| log_IL4_WK8 | 18 | 0.00 | -0.62, 0.61 | >0.9 |
| log_IL10_BL | 21 | 0.62 | 0.05, 1.2 | 0.034 |
| log_IL10_WK8 | 18 | -0.37 | -1.0, 0.22 | 0.2 |
| log_IL4Raox_7_16_BL | 30 | -0.44 | -1.4, 0.50 | 0.4 |
| log_IL4Raox_7_16_WK8 | 30 | 0.08 | -0.66, 0.81 | 0.8 |
| log_IL10Raox_7_16_BL | 30 | 0.11 | -0.44, 0.66 | 0.7 |
| log_IL10Raox_7_16_WK8 | 30 | -0.61 | -2.0, 0.74 | 0.4 |
| log_FGF_BL | 15 | 0.13 | -0.28, 0.54 | 0.5 |
| log_FGF_WK8 | 9 | 0.15 | -0.20, 0.49 | 0.3 |
| log_VEGF-Elisa_BL | 31 | 0.03 | -0.28, 0.35 | 0.8 |
| log_VEGF-Elisa_WK8 | 30 | -0.03 | -0.31, 0.26 | 0.8 |
| log_VEGFRaoxold_BL | 21 | 0.21 | -0.11, 0.52 | 0.2 |
| log_VEGFRaoxold_WK8 | 18 | 0.12 | -0.24, 0.48 | 0.5 |
| log_VEGFRaoxnew_BL | 30 | 0.19 | -0.05, 0.43 | 0.11 |
| log_VEGFRaoxnew_WK8 | 30 | 0.11 | -0.14, 0.35 | 0.4 |
| log_EGF_BL | 21 | 0.13 | -0.18, 0.44 | 0.4 |
| log_EGF_WK8 | 18 | -0.22 | -0.58, 0.13 | 0.2 |
| log_EGFRaox_7_16_BL | 30 | 0.04 | -0.30, 0.39 | 0.8 |
| log_EGFRaox_7_16_WK8 | 30 | 0.19 | -0.18, 0.57 | 0.3 |
| log_KP_AA_BL | 39 | 0.30 | -0.02, 0.61 | 0.065 |
| log_KP_AA_WK8 | 37 | 0.16 | -0.10, 0.42 | 0.2 |
| log_KP_KynA_BL | 39 | 0.30 | -0.05, 0.65 | 0.093 |
| log_KP_KynA_WK8 | 37 | 0.20 | -0.15, 0.55 | 0.2 |
| log_KP_Trp_BL | 39 | -0.04 | -0.54, 0.46 | 0.9 |
| log_KP_Trp_WK8 | 37 | -0.04 | -0.55, 0.47 | 0.9 |
| log_KP_Kyn_BL | 39 | 0.35 | 0.01, 0.69 | 0.044 |
| log_KP_Kyn_WK8 | 37 | 0.19 | -0.18, 0.57 | 0.3 |
| log_KP_Xan_BL | 38 | 0.20 | -0.17, 0.56 | 0.3 |
| log_KP_Xan_WK8 | 33 | -0.08 | -0.44, 0.28 | 0.6 |
| log_KP_Pic_BL | 39 | -0.01 | -0.27, 0.24 | >0.9 |
| log_KP_Pic_WK8 | 37 | 0.05 | -0.15, 0.25 | 0.6 |
| log_KP_Quin_BL | 39 | 0.26 | 0.01, 0.50 | 0.039 |
| log_KP_Quin_WK8 | 37 | 0.29 | 0.02, 0.57 | 0.036 |
| log_KP_QuinaldA_BL | 39 | 0.30 | -0.07, 0.68 | 0.11 |
| log_KP_QuinaldA_WK8 | 37 | 0.33 | -0.10, 0.76 | 0.13 |
| log_KP_3HK_BL | 39 | 0.03 | -0.17, 0.24 | 0.7 |
| log_KP_3HK_WK8 | 37 | 0.07 | -0.17, 0.32 | 0.5 |
| 1 CI = Confidence Interval | ||||
16 SII by timepoint and remission
ggplot(SG_df_new_long, aes(x = Timepoint, y = log(SII)))+
geom_boxplot(aes(fill=Timepoint))+
geom_jitter(width = 0.1)+
facet_wrap(~Treatment)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=4)
ggplot(SG_df_new_long, aes(x = Timepoint, y = log(SII)))+
geom_boxplot(aes(fill=Timepoint))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=4)
17 SIRI by timepoint and remission
ggplot(SG_df_new_long, aes(x = Timepoint, y = log(SIRI)))+
geom_boxplot(aes(fill=Timepoint))+
geom_jitter(width = 0.1)+
facet_wrap(~Treatment)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=4)
ggplot(SG_df_new_long, aes(x = Timepoint, y = log(SIRI)))+
geom_boxplot(aes(fill=Timepoint))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=4)
18 MODEL 1A: SII by HAMD17*Timepoint
19 MODEL 1B: SIRI by HAMD17*Timepoint
20 MODEL 2A: HAMD17_WK8 by SII_BL
# SII_model<-lm(HAMD17_WK8~Arm+HAMD17_BL+log(SII_BL), data=combined_df)
# sjPlot::tab_model(SII_model)
SII_model<-lm(HAMD17_WK8~Sex+Age+log_BMI+Arm+HAMD17_BL+log_SII_BL, data=combined_df)
sjPlot::tab_model(SII_model)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -21.11 | -75.08 – 32.87 | 0.433 |
| Sex [Female] | 4.75 | 0.23 – 9.27 | 0.040 |
| Age | 0.11 | -0.07 – 0.30 | 0.216 |
| log BMI | 3.40 | -8.06 – 14.87 | 0.551 |
| Arm [CBX ESC] | -5.55 | -10.24 – -0.85 | 0.022 |
| HAMD17 BL | 0.29 | -0.06 – 0.64 | 0.100 |
| log SII BL | 1.75 | -2.90 – 6.40 | 0.450 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.332 / 0.220 | ||
21 MODEL 2B: HAMD17_WK8 by SIRI_BL
# SII_model<-lm(HAMD17_WK8~Arm+HAMD17_BL+log(SII_BL), data=combined_df)
# sjPlot::tab_model(SII_model)
SII_model<-lm(HAMD17_WK8~Sex+Age+log_BMI+Arm+HAMD17_BL+log_SIRI_BL, data=combined_df)
sjPlot::tab_model(SII_model)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -15.64 | -56.10 – 24.83 | 0.438 |
| Sex [Female] | 4.39 | 0.15 – 8.63 | 0.043 |
| Age | 0.12 | -0.06 – 0.29 | 0.198 |
| log BMI | 4.61 | -6.91 – 16.14 | 0.422 |
| Arm [CBX ESC] | -5.30 | -9.89 – -0.71 | 0.025 |
| HAMD17 BL | 0.34 | -0.02 – 0.69 | 0.061 |
| log SIRI BL | 2.48 | -1.34 – 6.29 | 0.196 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.352 / 0.244 | ||
22 MODEL 3A: HAMD17_WK8 by SII_BL*interaction
# SII_model_interaction<-lm(HAMD17_WK8~Arm+HAMD17_BL+SII_BL*Age, data=combined_df)
# # plot(SII_model_interaction, which=c(2,6))
# sjPlot::tab_model(SII_model_interaction)
# reg_cohend(SII_model_interaction)
# interactions::interact_plot(SII_model_interaction, pred = SII_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SII-to-Age interaction")
SII_model_interaction<-lm(HAMD17_WK8~Sex+Age+log_BMI+Arm+HAMD17_BL+log_SII_BL*Age, data=combined_df)
# plot(SII_model_interaction, which=c(2,6))
sjPlot::tab_model(SII_model_interaction)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 87.27 | 4.18 – 170.37 | 0.040 |
| Sex [Female] | 4.33 | 0.30 – 8.36 | 0.036 |
| Age | -2.47 | -4.10 – -0.85 | 0.004 |
| log BMI | 6.21 | -4.15 – 16.56 | 0.232 |
| Arm [CBX ESC] | -4.66 | -8.88 – -0.45 | 0.031 |
| HAMD17 BL | 0.12 | -0.20 – 0.45 | 0.445 |
| log SII BL | -17.12 | -29.63 – -4.61 | 0.009 |
| Age * log SII BL | 0.43 | 0.16 – 0.70 | 0.003 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.486 / 0.384 | ||
23 MODEL 3B: HAMD17_WK8 by SIRI_BL*interaction
# SII_model_interaction<-lm(HAMD17_WK8~Arm+HAMD17_BL+SII_BL*Age, data=combined_df)
# # plot(SII_model_interaction, which=c(2,6))
# sjPlot::tab_model(SII_model_interaction)
# reg_cohend(SII_model_interaction)
# interactions::interact_plot(SII_model_interaction, pred = SII_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SII-to-Age interaction")
SII_model_interaction<-lm(HAMD17_WK8~Sex+Age+log_BMI+Arm+HAMD17_BL*log_SIRI_BL, data=combined_df)
# plot(SII_model_interaction, which=c(2,6))
sjPlot::tab_model(SII_model_interaction)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | -10.29 | -47.75 – 27.17 | 0.581 |
| Sex [Female] | 6.02 | 1.93 – 10.11 | 0.005 |
| Age | 0.05 | -0.12 – 0.22 | 0.525 |
| log BMI | 3.31 | -7.35 – 13.96 | 0.533 |
| Arm [CBX ESC] | -5.13 | -9.36 – -0.90 | 0.019 |
| HAMD17 BL | 0.42 | 0.09 – 0.75 | 0.014 |
| log SIRI BL | -17.85 | -33.26 – -2.44 | 0.024 |
| HAMD17 BL * log SIRI BL | 0.91 | 0.24 – 1.59 | 0.009 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.467 / 0.361 | ||
24 MODEL 4A (reduced/final): HAMD17_WK8 by SII_BL*interaction
# SII_model_interaction<-lm(HAMD17_WK8~Arm+HAMD17_BL+SII_BL*Age, data=combined_df)
# # plot(SII_model_interaction, which=c(2,6))
# sjPlot::tab_model(SII_model_interaction)
# reg_cohend(SII_model_interaction)
# interactions::interact_plot(SII_model_interaction, pred = SII_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SII-to-Age interaction")
SII_model_interaction<-lm(HAMD17_WK8~Sex+Age+Arm+log_SII_BL*Age, data=combined_df)
# plot(SII_model_interaction, which=c(2,6))
sjPlot::tab_model(SII_model_interaction)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 117.65 | 43.97 – 191.34 | 0.003 |
| Sex [Female] | 3.68 | -0.28 – 7.63 | 0.067 |
| Age | -2.53 | -4.07 – -0.98 | 0.002 |
| Arm [CBX ESC] | -4.67 | -8.82 – -0.52 | 0.028 |
| log SII BL | -18.19 | -30.11 – -6.27 | 0.004 |
| Age * log SII BL | 0.44 | 0.19 – 0.70 | 0.001 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.449 / 0.375 | ||
interactions::interact_plot(SII_model_interaction, pred = log_SII_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SII-to-Age interaction")
25 MODEL 4B (reduced/final): HAMD17_WK8 by SIRI_BL*interaction
# SII_model_interaction<-lm(HAMD17_WK8~Arm+HAMD17_BL+SII_BL*Age, data=combined_df)
# # plot(SII_model_interaction, which=c(2,6))
# sjPlot::tab_model(SII_model_interaction)
# reg_cohend(SII_model_interaction)
# interactions::interact_plot(SII_model_interaction, pred = SII_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SII-to-Age interaction")
SII_model_interaction<-lm(HAMD17_WK8~Sex+Arm+HAMD17_BL*log_SIRI_BL, data=combined_df)
# plot(SII_model_interaction, which=c(2,6))
sjPlot::tab_model(SII_model_interaction)| Â | HAMD 17 WK 8 | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.16 | -5.39 – 11.72 | 0.459 |
| Sex [Female] | 6.05 | 2.06 – 10.05 | 0.004 |
| Arm [CBX ESC] | -5.76 | -9.66 – -1.87 | 0.005 |
| HAMD17 BL | 0.45 | 0.13 – 0.77 | 0.007 |
| log SIRI BL | -20.09 | -34.37 – -5.81 | 0.007 |
| HAMD17 BL * log SIRI BL | 0.99 | 0.36 – 1.63 | 0.003 |
| Observations | 43 | ||
| R2 / R2 adjusted | 0.454 / 0.380 | ||
interactions::interact_plot(SII_model_interaction, pred = log_SIRI_BL, modx = HAMD17_BL, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SIRI-to-HAMD17_BL interaction")