SIRI_publication
Stephen Murata
05/08/2023
1 Raw distributions: Demographics/CBC
2 Log distributions: Demo/CBC variables
SG_raw_transformed<-SG_raw %>% select(-Arm, -Sex, -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, -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, Group,Arm, Sex, Age, 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)
createTable(compareGroups(~., data=combined_df))##
## --------Summary descriptives table ---------
##
## __________________________________________
## [ALL] N
## N=89
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Subject_ID 653492 (941114) 89
## Group: 89
## HC 32 (36.0%)
## PT 57 (64.0%)
## Arm: 57
## PBO_ESC 26 (45.6%)
## CBX_ESC 31 (54.4%)
## Sex: 87
## Male 46 (52.9%)
## Female 41 (47.1%)
## Age 41.7 (12.9) 78
## HAMD17_BL 24.3 (6.13) 47
## HAMD17_WK8 11.7 (7.33) 47
## Remission: 47
## Non-remitter 33 (70.2%)
## Remitter 14 (29.8%)
## Response: 47
## Non-responder 19 (40.4%)
## Responder 28 (59.6%)
## log_BMI 3.38 (0.22) 75
## log_PLT_BL 5.44 (0.25) 85
## log_MONO_BL -0.78 (0.34) 83
## log_NEUT_BL 1.27 (0.38) 82
## log_LYMPH_BL 0.65 (0.26) 83
## log_PLT_WK8 5.38 (0.26) 52
## log_MONO_WK8 -0.78 (0.41) 51
## log_NEUT_WK8 1.30 (0.44) 51
## log_LYMPH_WK8 0.65 (0.35) 51
## log_SII_BL 6.06 (0.52) 82
## log_SII_WK8 6.00 (0.48) 48
## log_SIRI_BL -0.15 (0.62) 82
## log_SIRI_WK8 -0.14 (0.58) 48
## log_IL1A_BL 0.73 (0.06) 21
## log_IL1A_WK8 0.71 (0.04) 18
## log_IL1B_BL 0.78 (0.15) 22
## log_IL1B_WK8 0.81 (0.16) 18
## log_IL2_BL 0.82 (0.41) 21
## log_IL2_WK8 0.85 (0.47) 18
## log_IL6_BL 1.30 (0.37) 21
## log_IL6_WK8 1.26 (0.25) 20
## log_IL8_BL 1.57 (0.58) 21
## log_IL8_WK8 1.58 (0.76) 18
## log_IFNG_BL 0.85 (0.31) 21
## log_IFNG_WK8 0.77 (0.19) 18
## log_TNFA_BL 1.42 (0.74) 21
## log_TNFA_WK8 1.51 (0.74) 18
## log_MCP1_BL 4.63 (0.33) 21
## log_MCP1_WK8 4.54 (0.57) 18
## log_CRPSet1_ug_ml_BL 1.64 (0.67) 15
## log_CRPSet1_ug_ml_WK8 1.53 (0.75) 13
## log_CRPSet2_µg_ml_BL 1.43 (0.73) 32
## log_CRPSet2_µg_ml_WK8 1.53 (0.76) 31
## log_IL1ARaox_7_16_BL 0.85 (0.05) 30
## log_IL1ARaox_7_16_WK8 0.82 (0.03) 30
## log_IL1BRaox_7_16_BL 1.12 (0.13) 30
## log_IL1BRaox_7_16_WK8 1.09 (0.11) 30
## log_IL2-Raox_7_16_BL 1.50 (0.15) 25
## log_IL2-Raox_7_16_WK8 1.45 (0.16) 30
## log_IL6Raox_7_16_BL 1.19 (0.23) 30
## log_IL6Raox_7_16_WK8 1.16 (0.21) 31
## log_IL8Raox_7_16_BL 1.89 (0.93) 30
## log_IL8Raox_7_16_WK8 1.81 (0.90) 30
## log_IFNGRaox_7_16_BL 0.90 (0.12) 30
## log_IFNGRaox_7_16_Week8 0.88 (0.09) 30
## log_TNFARaox_7_16_BL 2.01 (1.40) 30
## log_TNFARaox_7_16_WK8 1.88 (1.32) 30
## log_MCP1Raox_7_16_BL 4.46 (0.54) 30
## log_MCP1Raox_7_16_WK8 4.45 (0.86) 30
## log_IL4_BL 1.14 (0.31) 26
## log_IL4_WK8 0.99 (0.27) 18
## log_IL10_BL 0.90 (0.23) 21
## log_IL10_WK8 0.94 (0.27) 18
## log_IL4Raox_7_16_BL 1.53 (0.13) 30
## log_IL4Raox_7_16_WK8 1.52 (0.16) 30
## log_IL10Raox_7_16_BL 1.09 (0.22) 30
## log_IL10Raox_7_16_WK8 1.06 (0.09) 30
## log_FGF_BL 1.25 (0.43) 15
## log_FGF_WK8 1.14 (0.58) 9
## log_VEGF-Elisa_BL 3.61 (0.37) 31
## log_VEGF-Elisa_WK8 3.68 (0.42) 30
## log_VEGFRaoxold_BL 3.32 (0.43) 21
## log_VEGFRaoxold_WK8 3.23 (0.46) 18
## log_VEGFRaoxnew_BL 2.75 (0.48) 30
## log_VEGFRaoxnew_WK8 2.69 (0.48) 30
## log_EGF_BL 1.25 (0.46) 21
## log_EGF_WK8 1.40 (0.45) 18
## log_EGFRaox_7_16_BL 1.73 (0.35) 30
## log_EGFRaox_7_16_WK8 1.64 (0.32) 30
## log_KP_AA_BL 1.64 (0.35) 43
## log_KP_AA_WK8 1.70 (0.45) 41
## log_KP_KynA_BL 2.25 (0.31) 43
## log_KP_KynA_WK8 2.20 (0.33) 41
## log_KP_Trp_BL 9.65 (0.23) 43
## log_KP_Trp_WK8 9.61 (0.23) 41
## log_KP_Kyn_BL 5.79 (0.33) 43
## log_KP_Kyn_WK8 5.73 (0.31) 41
## log_KP_Xan_BL 1.58 (0.31) 42
## log_KP_Xan_WK8 1.59 (0.35) 37
## log_KP_Pic_BL 3.05 (0.53) 43
## log_KP_Pic_WK8 3.05 (0.64) 41
## log_KP_Quin_BL 4.07 (0.45) 43
## log_KP_Quin_WK8 4.01 (0.41) 41
## log_KP_QuinaldA_BL 1.33 (0.30) 43
## log_KP_QuinaldA_WK8 1.32 (0.27) 41
## log_KP_3HK_BL 2.86 (0.55) 43
## log_KP_3HK_WK8 2.79 (0.49) 41
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯5 Group comparison by patient status
createTable(compareGroups(Group~., data=combined_df))##
## --------Summary descriptives table by 'Group'---------
##
## _______________________________________________________________
## HC PT p.overall
## N=32 N=57
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Subject_ID 1806032 (610918) 6452 (2982) <0.001
## Arm: .
## PBO_ESC 0 (.%) 26 (45.6%)
## CBX_ESC 0 (.%) 31 (54.4%)
## Sex: 0.016
## Male 11 (34.4%) 35 (63.6%)
## Female 21 (65.6%) 20 (36.4%)
## Age 39.4 (14.1) 42.7 (12.3) 0.336
## HAMD17_BL . (.) 24.3 (6.13) .
## HAMD17_WK8 . (.) 11.7 (7.33) .
## Remission: .
## Non-remitter 0 (.%) 33 (70.2%)
## Remitter 0 (.%) 14 (29.8%)
## Response: .
## Non-responder 0 (.%) 19 (40.4%)
## Responder 0 (.%) 28 (59.6%)
## log_BMI 3.23 (0.21) 3.43 (0.20) <0.001
## log_PLT_BL 5.47 (0.23) 5.42 (0.26) 0.334
## log_MONO_BL -0.81 (0.27) -0.76 (0.38) 0.472
## log_NEUT_BL 1.21 (0.36) 1.31 (0.38) 0.217
## log_LYMPH_BL 0.65 (0.24) 0.65 (0.28) 0.914
## log_PLT_WK8 . (.) 5.38 (0.26) .
## log_MONO_WK8 . (.) -0.78 (0.41) .
## log_NEUT_WK8 . (.) 1.30 (0.44) .
## log_LYMPH_WK8 . (.) 0.65 (0.35) .
## log_SII_BL 6.03 (0.54) 6.08 (0.51) 0.702
## log_SII_WK8 . (.) 6.00 (0.48) .
## log_SIRI_BL -0.25 (0.60) -0.08 (0.62) 0.212
## log_SIRI_WK8 . (.) -0.14 (0.58) .
## log_IL1A_BL . (.) 0.73 (0.06) .
## log_IL1A_WK8 . (.) 0.71 (0.04) .
## log_IL1B_BL . (.) 0.78 (0.15) .
## log_IL1B_WK8 . (.) 0.81 (0.16) .
## log_IL2_BL . (.) 0.82 (0.41) .
## log_IL2_WK8 . (.) 0.85 (0.47) .
## log_IL6_BL . (.) 1.30 (0.37) .
## log_IL6_WK8 . (.) 1.26 (0.25) .
## log_IL8_BL . (.) 1.57 (0.58) .
## log_IL8_WK8 . (.) 1.58 (0.76) .
## log_IFNG_BL . (.) 0.85 (0.31) .
## log_IFNG_WK8 . (.) 0.77 (0.19) .
## log_TNFA_BL . (.) 1.42 (0.74) .
## log_TNFA_WK8 . (.) 1.51 (0.74) .
## log_MCP1_BL . (.) 4.63 (0.33) .
## log_MCP1_WK8 . (.) 4.54 (0.57) .
## log_CRPSet1_ug_ml_BL . (.) 1.64 (0.67) .
## log_CRPSet1_ug_ml_WK8 . (.) 1.53 (0.75) .
## log_CRPSet2_µg_ml_BL . (.) 1.43 (0.73) .
## log_CRPSet2_µg_ml_WK8 . (.) 1.53 (0.76) .
## log_IL1ARaox_7_16_BL . (.) 0.85 (0.05) .
## log_IL1ARaox_7_16_WK8 . (.) 0.82 (0.03) .
## log_IL1BRaox_7_16_BL . (.) 1.12 (0.13) .
## log_IL1BRaox_7_16_WK8 . (.) 1.09 (0.11) .
## log_IL2-Raox_7_16_BL . (.) 1.50 (0.15) .
## log_IL2-Raox_7_16_WK8 . (.) 1.45 (0.16) .
## log_IL6Raox_7_16_BL . (.) 1.19 (0.23) .
## log_IL6Raox_7_16_WK8 . (.) 1.16 (0.21) .
## log_IL8Raox_7_16_BL . (.) 1.89 (0.93) .
## log_IL8Raox_7_16_WK8 . (.) 1.81 (0.90) .
## log_IFNGRaox_7_16_BL . (.) 0.90 (0.12) .
## log_IFNGRaox_7_16_Week8 . (.) 0.88 (0.09) .
## log_TNFARaox_7_16_BL . (.) 2.01 (1.40) .
## log_TNFARaox_7_16_WK8 . (.) 1.88 (1.32) .
## log_MCP1Raox_7_16_BL . (.) 4.46 (0.54) .
## log_MCP1Raox_7_16_WK8 . (.) 4.45 (0.86) .
## log_IL4_BL . (.) 1.14 (0.31) .
## log_IL4_WK8 . (.) 0.99 (0.27) .
## log_IL10_BL . (.) 0.90 (0.23) .
## log_IL10_WK8 . (.) 0.94 (0.27) .
## log_IL4Raox_7_16_BL . (.) 1.53 (0.13) .
## log_IL4Raox_7_16_WK8 . (.) 1.52 (0.16) .
## log_IL10Raox_7_16_BL . (.) 1.09 (0.22) .
## log_IL10Raox_7_16_WK8 . (.) 1.06 (0.09) .
## log_FGF_BL . (.) 1.25 (0.43) .
## log_FGF_WK8 . (.) 1.14 (0.58) .
## log_VEGF-Elisa_BL . (.) 3.61 (0.37) .
## log_VEGF-Elisa_WK8 . (.) 3.68 (0.42) .
## log_VEGFRaoxold_BL . (.) 3.32 (0.43) .
## log_VEGFRaoxold_WK8 . (.) 3.23 (0.46) .
## log_VEGFRaoxnew_BL . (.) 2.75 (0.48) .
## log_VEGFRaoxnew_WK8 . (.) 2.69 (0.48) .
## log_EGF_BL . (.) 1.25 (0.46) .
## log_EGF_WK8 . (.) 1.40 (0.45) .
## log_EGFRaox_7_16_BL . (.) 1.73 (0.35) .
## log_EGFRaox_7_16_WK8 . (.) 1.64 (0.32) .
## log_KP_AA_BL . (.) 1.64 (0.35) .
## log_KP_AA_WK8 . (.) 1.70 (0.45) .
## log_KP_KynA_BL . (.) 2.25 (0.31) .
## log_KP_KynA_WK8 . (.) 2.20 (0.33) .
## log_KP_Trp_BL . (.) 9.65 (0.23) .
## log_KP_Trp_WK8 . (.) 9.61 (0.23) .
## log_KP_Kyn_BL . (.) 5.79 (0.33) .
## log_KP_Kyn_WK8 . (.) 5.73 (0.31) .
## log_KP_Xan_BL . (.) 1.58 (0.31) .
## log_KP_Xan_WK8 . (.) 1.59 (0.35) .
## log_KP_Pic_BL . (.) 3.05 (0.53) .
## log_KP_Pic_WK8 . (.) 3.05 (0.64) .
## log_KP_Quin_BL . (.) 4.07 (0.45) .
## log_KP_Quin_WK8 . (.) 4.01 (0.41) .
## log_KP_QuinaldA_BL . (.) 1.33 (0.30) .
## log_KP_QuinaldA_WK8 . (.) 1.32 (0.27) .
## log_KP_3HK_BL . (.) 2.86 (0.55) .
## log_KP_3HK_WK8 . (.) 2.79 (0.49) .
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯6 Group comparison by treatment arm status
createTable(compareGroups(Arm~., data=combined_df))##
## --------Summary descriptives table by 'Arm'---------
##
## ___________________________________________________________
## PBO_ESC CBX_ESC p.overall
## N=26 N=31
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Subject_ID 6795 (3022) 6164 (2966) 0.432
## Group: PT 26 (100%) 31 (100%) .
## Sex: 0.739
## Male 17 (68.0%) 18 (60.0%)
## Female 8 (32.0%) 12 (40.0%)
## Age 46.6 (12.5) 39.4 (11.4) 0.033
## HAMD17_BL 23.4 (6.24) 25.0 (6.08) 0.411
## HAMD17_WK8 15.0 (7.41) 9.19 (6.33) 0.007
## Remission: 0.004
## Non-remitter 19 (95.0%) 14 (51.9%)
## Remitter 1 (5.00%) 13 (48.1%)
## Response: 0.040
## Non-responder 12 (60.0%) 7 (25.9%)
## Responder 8 (40.0%) 20 (74.1%)
## log_BMI 3.46 (0.17) 3.42 (0.22) 0.472
## log_PLT_BL 5.46 (0.30) 5.39 (0.22) 0.353
## log_MONO_BL -0.75 (0.43) -0.77 (0.34) 0.808
## log_NEUT_BL 1.35 (0.41) 1.28 (0.37) 0.522
## log_LYMPH_BL 0.63 (0.30) 0.67 (0.27) 0.653
## log_PLT_WK8 5.36 (0.31) 5.39 (0.21) 0.669
## log_MONO_WK8 -0.74 (0.43) -0.81 (0.39) 0.537
## log_NEUT_WK8 1.34 (0.52) 1.28 (0.37) 0.624
## log_LYMPH_WK8 0.63 (0.35) 0.67 (0.35) 0.633
## log_SII_BL 6.17 (0.52) 6.01 (0.51) 0.258
## log_SII_WK8 6.05 (0.49) 5.95 (0.47) 0.444
## log_SIRI_BL -0.03 (0.66) -0.12 (0.60) 0.598
## log_SIRI_WK8 -0.02 (0.60) -0.25 (0.56) 0.191
## log_IL1A_BL 0.75 (0.06) 0.72 (0.06) 0.394
## log_IL1A_WK8 0.71 (0.04) 0.71 (0.04) 0.869
## log_IL1B_BL 0.80 (0.15) 0.77 (0.16) 0.617
## log_IL1B_WK8 0.80 (0.14) 0.81 (0.17) 0.850
## log_IL2_BL 0.87 (0.50) 0.79 (0.36) 0.710
## log_IL2_WK8 0.69 (0.00) 0.91 (0.55) 0.170
## log_IL6_BL 1.43 (0.37) 1.23 (0.37) 0.242
## log_IL6_WK8 1.24 (0.21) 1.27 (0.27) 0.750
## log_IL8_BL 1.61 (0.56) 1.55 (0.61) 0.833
## log_IL8_WK8 1.73 (0.94) 1.53 (0.71) 0.683
## log_IFNG_BL 0.83 (0.26) 0.86 (0.35) 0.805
## log_IFNG_WK8 0.69 (0.00) 0.80 (0.21) 0.084
## log_TNFA_BL 1.46 (0.87) 1.40 (0.70) 0.856
## log_TNFA_WK8 1.86 (1.25) 1.37 (0.41) 0.432
## log_MCP1_BL 4.69 (0.35) 4.60 (0.33) 0.533
## log_MCP1_WK8 4.46 (0.63) 4.57 (0.57) 0.750
## log_CRPSet1_ug_ml_BL 1.40 (0.36) 1.84 (0.83) 0.206
## log_CRPSet1_ug_ml_WK8 1.43 (0.81) 1.56 (0.78) 0.807
## log_CRPSet2_µg_ml_BL 1.44 (0.68) 1.42 (0.77) 0.955
## log_CRPSet2_µg_ml_WK8 2.09 (0.69) 1.27 (0.65) 0.006
## log_IL1ARaox_7_16_BL 0.86 (0.07) 0.84 (0.04) 0.276
## log_IL1ARaox_7_16_WK8 0.83 (0.03) 0.82 (0.03) 0.442
## log_IL1BRaox_7_16_BL 1.15 (0.14) 1.10 (0.13) 0.398
## log_IL1BRaox_7_16_WK8 1.16 (0.13) 1.06 (0.09) 0.074
## log_IL2-Raox_7_16_BL 1.47 (0.13) 1.51 (0.16) 0.479
## log_IL2-Raox_7_16_WK8 1.48 (0.15) 1.44 (0.16) 0.544
## log_IL6Raox_7_16_BL 1.17 (0.21) 1.19 (0.25) 0.855
## log_IL6Raox_7_16_WK8 1.12 (0.21) 1.18 (0.21) 0.498
## log_IL8Raox_7_16_BL 1.87 (1.07) 1.90 (0.88) 0.942
## log_IL8Raox_7_16_WK8 1.76 (0.69) 1.82 (0.99) 0.851
## log_IFNGRaox_7_16_BL 0.94 (0.16) 0.88 (0.09) 0.339
## log_IFNGRaox_7_16_Week8 0.88 (0.05) 0.88 (0.10) 0.989
## log_TNFARaox_7_16_BL 1.94 (1.56) 2.04 (1.36) 0.858
## log_TNFARaox_7_16_WK8 2.12 (1.46) 1.78 (1.28) 0.553
## log_MCP1Raox_7_16_BL 4.46 (0.47) 4.46 (0.58) 0.992
## log_MCP1Raox_7_16_WK8 4.44 (0.56) 4.45 (0.97) 0.961
## log_IL4_BL 1.11 (0.39) 1.16 (0.26) 0.698
## log_IL4_WK8 1.03 (0.31) 0.97 (0.27) 0.729
## log_IL10_BL 0.97 (0.24) 0.85 (0.21) 0.261
## log_IL10_WK8 0.79 (0.22) 1.00 (0.27) 0.136
## log_IL4Raox_7_16_BL 1.51 (0.10) 1.55 (0.14) 0.427
## log_IL4Raox_7_16_WK8 1.54 (0.13) 1.51 (0.18) 0.620
## log_IL10Raox_7_16_BL 1.13 (0.35) 1.08 (0.12) 0.679
## log_IL10Raox_7_16_WK8 1.07 (0.09) 1.05 (0.09) 0.595
## log_FGF_BL 1.30 (0.38) 1.20 (0.50) 0.679
## log_FGF_WK8 0.69 (0.00) 1.27 (0.60) 0.045
## log_VEGF-Elisa_BL 3.47 (0.28) 3.68 (0.39) 0.087
## log_VEGF-Elisa_WK8 3.66 (0.46) 3.69 (0.42) 0.850
## log_VEGFRaoxold_BL 3.38 (0.61) 3.28 (0.30) 0.682
## log_VEGFRaoxold_WK8 3.29 (0.61) 3.21 (0.41) 0.779
## log_VEGFRaoxnew_BL 2.71 (0.55) 2.77 (0.46) 0.793
## log_VEGFRaoxnew_WK8 2.68 (0.56) 2.69 (0.47) 0.970
## log_EGF_BL 1.33 (0.43) 1.20 (0.49) 0.545
## log_EGF_WK8 1.45 (0.32) 1.38 (0.50) 0.758
## log_EGFRaox_7_16_BL 1.82 (0.43) 1.68 (0.30) 0.359
## log_EGFRaox_7_16_WK8 1.69 (0.29) 1.62 (0.33) 0.562
## log_KP_AA_BL 1.74 (0.41) 1.57 (0.28) 0.141
## log_KP_AA_WK8 1.78 (0.48) 1.65 (0.43) 0.370
## log_KP_KynA_BL 2.23 (0.27) 2.27 (0.34) 0.710
## log_KP_KynA_WK8 2.12 (0.28) 2.25 (0.36) 0.201
## log_KP_Trp_BL 9.60 (0.20) 9.69 (0.24) 0.171
## log_KP_Trp_WK8 9.57 (0.21) 9.64 (0.25) 0.373
## log_KP_Kyn_BL 5.83 (0.34) 5.76 (0.33) 0.456
## log_KP_Kyn_WK8 5.78 (0.24) 5.70 (0.35) 0.394
## log_KP_Xan_BL 1.55 (0.25) 1.61 (0.35) 0.521
## log_KP_Xan_WK8 1.43 (0.24) 1.70 (0.37) 0.013
## log_KP_Pic_BL 3.02 (0.61) 3.08 (0.47) 0.754
## log_KP_Pic_WK8 2.98 (0.76) 3.09 (0.55) 0.622
## log_KP_Quin_BL 4.17 (0.51) 3.99 (0.39) 0.223
## log_KP_Quin_WK8 4.05 (0.49) 3.99 (0.36) 0.650
## log_KP_QuinaldA_BL 1.27 (0.36) 1.37 (0.25) 0.317
## log_KP_QuinaldA_WK8 1.24 (0.27) 1.38 (0.26) 0.129
## log_KP_3HK_BL 2.92 (0.51) 2.82 (0.59) 0.555
## log_KP_3HK_WK8 2.81 (0.51) 2.77 (0.49) 0.813
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯7 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)
8 Group comparison by response status
createTable(compareGroups(Response~., data=combined_df))##
## --------Summary descriptives table by 'Response'---------
##
## ____________________________________________________________
## Non-responder Responder p.overall
## N=19 N=28
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Subject_ID 6668 (3393) 6101 (2774) 0.550
## Group: PT 19 (100%) 28 (100%) .
## Arm: 0.040
## PBO_ESC 12 (63.2%) 8 (28.6%)
## CBX_ESC 7 (36.8%) 20 (71.4%)
## Sex: 0.271
## Male 9 (47.4%) 19 (67.9%)
## Female 10 (52.6%) 9 (32.1%)
## Age 45.9 (12.4) 39.9 (11.8) 0.101
## HAMD17_BL 22.5 (5.65) 25.6 (6.21) 0.084
## HAMD17_WK8 18.3 (6.31) 7.18 (3.67) <0.001
## Remission: 0.001
## Non-remitter 19 (100%) 14 (50.0%)
## Remitter 0 (0.00%) 14 (50.0%)
## log_BMI 3.45 (0.19) 3.43 (0.22) 0.689
## log_PLT_BL 5.38 (0.31) 5.42 (0.22) 0.600
## log_MONO_BL -0.63 (0.35) -0.81 (0.32) 0.108
## log_NEUT_BL 1.35 (0.32) 1.28 (0.40) 0.541
## log_LYMPH_BL 0.64 (0.30) 0.65 (0.28) 0.936
## log_PLT_WK8 5.26 (0.32) 5.43 (0.22) 0.072
## log_MONO_WK8 -0.63 (0.40) -0.83 (0.38) 0.117
## log_NEUT_WK8 1.46 (0.45) 1.25 (0.36) 0.133
## log_LYMPH_WK8 0.67 (0.36) 0.63 (0.35) 0.729
## log_SII_BL 6.08 (0.49) 6.06 (0.52) 0.899
## log_SII_WK8 5.94 (0.51) 6.04 (0.49) 0.549
## log_SIRI_BL 0.08 (0.52) -0.17 (0.64) 0.163
## log_SIRI_WK8 0.05 (0.51) -0.22 (0.59) 0.134
## log_IL1A_BL 0.74 (0.06) 0.73 (0.06) 0.727
## log_IL1A_WK8 0.69 (0.00) 0.72 (0.05) 0.082
## log_IL1B_BL 0.78 (0.15) 0.79 (0.16) 0.963
## log_IL1B_WK8 0.76 (0.14) 0.81 (0.17) 0.622
## log_IL2_BL 0.89 (0.53) 0.79 (0.36) 0.660
## log_IL2_WK8 0.69 (0.00) 0.91 (0.55) 0.170
## log_IL6_BL 1.37 (0.38) 1.23 (0.37) 0.458
## log_IL6_WK8 1.20 (0.25) 1.27 (0.27) 0.638
## log_IL8_BL 1.82 (0.73) 1.45 (0.49) 0.262
## log_IL8_WK8 1.84 (1.05) 1.54 (0.70) 0.627
## log_IFNG_BL 1.00 (0.42) 0.78 (0.23) 0.240
## log_IFNG_WK8 0.69 (0.00) 0.80 (0.21) 0.084
## log_TNFA_BL 1.88 (1.18) 1.20 (0.21) 0.182
## log_TNFA_WK8 2.10 (1.29) 1.35 (0.44) 0.328
## log_MCP1_BL 4.57 (0.45) 4.66 (0.29) 0.664
## log_MCP1_WK8 4.18 (0.87) 4.63 (0.46) 0.383
## log_CRPSet1_ug_ml_BL 1.36 (0.42) 1.80 (0.79) 0.199
## log_CRPSet1_ug_ml_WK8 1.31 (0.28) 1.50 (0.81) 0.588
## log_CRPSet2_µg_ml_BL 1.33 (0.81) 1.46 (0.72) 0.672
## log_CRPSet2_µg_ml_WK8 1.60 (0.90) 1.46 (0.71) 0.692
## log_IL1ARaox_7_16_BL 0.85 (0.07) 0.84 (0.03) 0.687
## log_IL1ARaox_7_16_WK8 0.81 (0.03) 0.83 (0.03) 0.427
## log_IL1BRaox_7_16_BL 1.15 (0.15) 1.10 (0.13) 0.368
## log_IL1BRaox_7_16_WK8 1.11 (0.11) 1.08 (0.11) 0.529
## log_IL2-Raox_7_16_BL 1.46 (0.09) 1.52 (0.17) 0.320
## log_IL2-Raox_7_16_WK8 1.41 (0.11) 1.47 (0.17) 0.284
## log_IL6Raox_7_16_BL 1.14 (0.21) 1.21 (0.25) 0.433
## log_IL6Raox_7_16_WK8 1.11 (0.22) 1.18 (0.20) 0.397
## log_IL8Raox_7_16_BL 2.30 (1.38) 1.69 (0.54) 0.202
## log_IL8Raox_7_16_WK8 2.17 (1.41) 1.65 (0.55) 0.309
## log_IFNGRaox_7_16_BL 0.95 (0.17) 0.88 (0.07) 0.230
## log_IFNGRaox_7_16_Week8 0.88 (0.10) 0.88 (0.09) 0.980
## log_TNFARaox_7_16_BL 2.70 (1.97) 1.66 (0.89) 0.141
## log_TNFARaox_7_16_WK8 2.66 (1.97) 1.55 (0.77) 0.137
## log_MCP1Raox_7_16_BL 4.24 (0.78) 4.58 (0.33) 0.217
## log_MCP1Raox_7_16_WK8 4.13 (0.91) 4.58 (0.82) 0.225
## log_IL4_BL 1.21 (0.34) 1.10 (0.30) 0.443
## log_IL4_WK8 0.96 (0.31) 1.02 (0.27) 0.739
## log_IL10_BL 0.96 (0.26) 0.85 (0.20) 0.330
## log_IL10_WK8 0.88 (0.38) 0.98 (0.25) 0.673
## log_IL4Raox_7_16_BL 1.44 (0.14) 1.58 (0.09) 0.011
## log_IL4Raox_7_16_WK8 1.47 (0.19) 1.53 (0.15) 0.404
## log_IL10Raox_7_16_BL 1.09 (0.36) 1.10 (0.11) 0.915
## log_IL10Raox_7_16_WK8 1.03 (0.08) 1.08 (0.09) 0.163
## log_FGF_BL 1.47 (0.44) 1.19 (0.40) 0.280
## log_FGF_WK8 0.70 (0.01) 1.36 (0.60) 0.044
## log_VEGF-Elisa_BL 3.73 (0.52) 3.56 (0.27) 0.345
## log_VEGF-Elisa_WK8 3.86 (0.56) 3.60 (0.34) 0.235
## log_VEGFRaoxold_BL 3.49 (0.60) 3.23 (0.32) 0.307
## log_VEGFRaoxold_WK8 3.34 (0.67) 3.19 (0.42) 0.695
## log_VEGFRaoxnew_BL 2.86 (0.69) 2.69 (0.34) 0.493
## log_VEGFRaoxnew_WK8 2.73 (0.70) 2.67 (0.38) 0.831
## log_EGF_BL 1.40 (0.42) 1.13 (0.46) 0.203
## log_EGF_WK8 1.08 (0.46) 1.46 (0.41) 0.201
## log_EGFRaox_7_16_BL 1.65 (0.28) 1.77 (0.38) 0.321
## log_EGFRaox_7_16_WK8 1.54 (0.27) 1.69 (0.33) 0.229
## log_KP_AA_BL 1.68 (0.38) 1.61 (0.32) 0.525
## log_KP_AA_WK8 1.70 (0.46) 1.70 (0.45) 0.976
## log_KP_KynA_BL 2.29 (0.34) 2.23 (0.30) 0.559
## log_KP_KynA_WK8 2.24 (0.33) 2.18 (0.34) 0.583
## log_KP_Trp_BL 9.61 (0.18) 9.67 (0.25) 0.372
## log_KP_Trp_WK8 9.62 (0.24) 9.60 (0.23) 0.817
## log_KP_Kyn_BL 5.81 (0.36) 5.77 (0.31) 0.745
## log_KP_Kyn_WK8 5.77 (0.24) 5.71 (0.34) 0.532
## log_KP_Xan_BL 1.59 (0.40) 1.58 (0.25) 0.936
## log_KP_Xan_WK8 1.57 (0.41) 1.60 (0.31) 0.788
## log_KP_Pic_BL 3.10 (0.49) 3.02 (0.56) 0.621
## log_KP_Pic_WK8 3.05 (0.70) 3.05 (0.61) 0.990
## log_KP_Quin_BL 4.15 (0.51) 4.01 (0.40) 0.339
## log_KP_Quin_WK8 4.14 (0.44) 3.95 (0.39) 0.174
## log_KP_QuinaldA_BL 1.38 (0.41) 1.29 (0.19) 0.424
## log_KP_QuinaldA_WK8 1.35 (0.33) 1.31 (0.23) 0.695
## log_KP_3HK_BL 2.90 (0.57) 2.83 (0.55) 0.685
## log_KP_3HK_WK8 2.92 (0.56) 2.72 (0.45) 0.246
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯9 Group comparison by remission status
createTable(compareGroups(Remission~., data=combined_df))##
## --------Summary descriptives table by 'Remission'---------
##
## ___________________________________________________________
## Non-remitter Remitter p.overall
## N=33 N=14
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Subject_ID 6178 (3286) 6689 (2330) 0.550
## Group: PT 33 (100%) 14 (100%) .
## Arm: 0.004
## PBO_ESC 19 (57.6%) 1 (7.14%)
## CBX_ESC 14 (42.4%) 13 (92.9%)
## Sex: 0.451
## Male 18 (54.5%) 10 (71.4%)
## Female 15 (45.5%) 4 (28.6%)
## Age 42.9 (12.8) 41.0 (11.4) 0.623
## HAMD17_BL 25.3 (6.49) 22.1 (4.62) 0.064
## HAMD17_WK8 14.8 (6.35) 4.21 (2.46) <0.001
## Response: 0.001
## Non-responder 19 (57.6%) 0 (0.00%)
## Responder 14 (42.4%) 14 (100%)
## log_BMI 3.47 (0.17) 3.38 (0.26) 0.255
## log_PLT_BL 5.39 (0.27) 5.44 (0.22) 0.565
## log_MONO_BL -0.74 (0.35) -0.75 (0.34) 0.905
## log_NEUT_BL 1.32 (0.35) 1.29 (0.43) 0.882
## log_LYMPH_BL 0.64 (0.30) 0.64 (0.27) 0.924
## log_PLT_WK8 5.34 (0.29) 5.42 (0.21) 0.305
## log_MONO_WK8 -0.70 (0.38) -0.88 (0.41) 0.194
## log_NEUT_WK8 1.38 (0.43) 1.22 (0.33) 0.202
## log_LYMPH_WK8 0.69 (0.33) 0.54 (0.37) 0.228
## log_SII_BL 6.06 (0.45) 6.09 (0.63) 0.867
## log_SII_WK8 5.96 (0.47) 6.08 (0.57) 0.532
## log_SIRI_BL -0.06 (0.53) -0.09 (0.78) 0.917
## log_SIRI_WK8 -0.08 (0.51) -0.21 (0.72) 0.568
## log_IL1A_BL 0.75 (0.07) 0.71 (0.04) 0.150
## log_IL1A_WK8 0.70 (0.03) 0.72 (0.05) 0.384
## log_IL1B_BL 0.77 (0.13) 0.83 (0.22) 0.554
## log_IL1B_WK8 0.77 (0.12) 0.84 (0.20) 0.444
## log_IL2_BL 0.79 (0.38) 0.91 (0.53) 0.644
## log_IL2_WK8 0.86 (0.52) 0.87 (0.47) 0.953
## log_IL6_BL 1.37 (0.39) 1.07 (0.24) 0.048
## log_IL6_WK8 1.26 (0.28) 1.24 (0.23) 0.862
## log_IL8_BL 1.63 (0.55) 1.47 (0.73) 0.635
## log_IL8_WK8 1.49 (0.70) 1.78 (0.89) 0.482
## log_IFNG_BL 0.93 (0.36) 0.69 (0.00) 0.028
## log_IFNG_WK8 0.74 (0.14) 0.84 (0.25) 0.356
## log_TNFA_BL 1.54 (0.88) 1.20 (0.30) 0.211
## log_TNFA_WK8 1.56 (0.91) 1.49 (0.53) 0.849
## log_MCP1_BL 4.68 (0.39) 4.50 (0.18) 0.162
## log_MCP1_WK8 4.42 (0.65) 4.68 (0.47) 0.350
## log_CRPSet1_ug_ml_BL 1.54 (0.58) 1.88 (1.00) 0.566
## log_CRPSet1_ug_ml_WK8 1.55 (0.72) 1.35 (0.85) 0.690
## log_CRPSet2_µg_ml_BL 1.52 (0.76) 1.21 (0.68) 0.273
## log_CRPSet2_µg_ml_WK8 1.58 (0.79) 1.35 (0.70) 0.427
## log_IL1ARaox_7_16_BL 0.84 (0.06) 0.85 (0.02) 0.677
## log_IL1ARaox_7_16_WK8 0.82 (0.03) 0.83 (0.03) 0.524
## log_IL1BRaox_7_16_BL 1.11 (0.13) 1.13 (0.15) 0.730
## log_IL1BRaox_7_16_WK8 1.10 (0.11) 1.08 (0.12) 0.763
## log_IL2-Raox_7_16_BL 1.46 (0.12) 1.59 (0.19) 0.102
## log_IL2-Raox_7_16_WK8 1.44 (0.14) 1.48 (0.20) 0.612
## log_IL6Raox_7_16_BL 1.21 (0.26) 1.13 (0.15) 0.272
## log_IL6Raox_7_16_WK8 1.16 (0.22) 1.15 (0.19) 0.914
## log_IL8Raox_7_16_BL 1.93 (1.02) 1.81 (0.73) 0.718
## log_IL8Raox_7_16_WK8 1.81 (1.00) 1.80 (0.72) 0.976
## log_IFNGRaox_7_16_BL 0.91 (0.13) 0.87 (0.08) 0.270
## log_IFNGRaox_7_16_Week8 0.88 (0.08) 0.88 (0.11) 0.860
## log_TNFARaox_7_16_BL 2.03 (1.52) 1.96 (1.18) 0.889
## log_TNFARaox_7_16_WK8 1.93 (1.45) 1.78 (1.08) 0.740
## log_MCP1Raox_7_16_BL 4.44 (0.60) 4.51 (0.36) 0.710
## log_MCP1Raox_7_16_WK8 4.37 (0.70) 4.61 (1.14) 0.546
## log_IL4_BL 1.16 (0.33) 1.09 (0.28) 0.615
## log_IL4_WK8 0.96 (0.28) 1.07 (0.26) 0.425
## log_IL10_BL 0.88 (0.23) 0.89 (0.22) 0.937
## log_IL10_WK8 0.94 (0.33) 0.98 (0.19) 0.777
## log_IL4Raox_7_16_BL 1.50 (0.13) 1.62 (0.09) 0.008
## log_IL4Raox_7_16_WK8 1.50 (0.15) 1.54 (0.19) 0.625
## log_IL10Raox_7_16_BL 1.09 (0.25) 1.10 (0.12) 0.885
## log_IL10Raox_7_16_WK8 1.06 (0.09) 1.07 (0.09) 0.683
## log_FGF_BL 1.26 (0.48) 1.35 (0.28) 0.667
## log_FGF_WK8 1.19 (0.70) 1.21 (0.15) 0.965
## log_VEGF-Elisa_BL 3.64 (0.39) 3.55 (0.31) 0.469
## log_VEGF-Elisa_WK8 3.74 (0.43) 3.55 (0.40) 0.247
## log_VEGFRaoxold_BL 3.37 (0.50) 3.20 (0.26) 0.347
## log_VEGFRaoxold_WK8 3.22 (0.55) 3.23 (0.38) 0.945
## log_VEGFRaoxnew_BL 2.75 (0.55) 2.74 (0.32) 0.928
## log_VEGFRaoxnew_WK8 2.70 (0.55) 2.67 (0.35) 0.862
## log_EGF_BL 1.25 (0.44) 1.16 (0.53) 0.723
## log_EGF_WK8 1.27 (0.35) 1.51 (0.55) 0.336
## log_EGFRaox_7_16_BL 1.68 (0.36) 1.83 (0.31) 0.271
## log_EGFRaox_7_16_WK8 1.59 (0.26) 1.75 (0.40) 0.252
## log_KP_AA_BL 1.64 (0.39) 1.63 (0.25) 0.882
## log_KP_AA_WK8 1.76 (0.53) 1.59 (0.21) 0.162
## log_KP_KynA_BL 2.25 (0.34) 2.25 (0.27) 1.000
## log_KP_KynA_WK8 2.20 (0.32) 2.20 (0.37) 0.974
## log_KP_Trp_BL 9.62 (0.21) 9.71 (0.27) 0.273
## log_KP_Trp_WK8 9.60 (0.23) 9.62 (0.24) 0.803
## log_KP_Kyn_BL 5.76 (0.34) 5.84 (0.31) 0.501
## log_KP_Kyn_WK8 5.73 (0.29) 5.74 (0.36) 0.997
## log_KP_Xan_BL 1.58 (0.35) 1.58 (0.24) 0.992
## log_KP_Xan_WK8 1.53 (0.33) 1.72 (0.35) 0.125
## log_KP_Pic_BL 3.08 (0.43) 2.99 (0.70) 0.645
## log_KP_Pic_WK8 3.10 (0.59) 2.95 (0.73) 0.512
## log_KP_Quin_BL 4.11 (0.46) 3.97 (0.41) 0.328
## log_KP_Quin_WK8 4.07 (0.45) 3.91 (0.32) 0.190
## log_KP_QuinaldA_BL 1.31 (0.34) 1.36 (0.20) 0.488
## log_KP_QuinaldA_WK8 1.28 (0.27) 1.40 (0.25) 0.179
## log_KP_3HK_BL 2.86 (0.58) 2.85 (0.51) 0.932
## log_KP_3HK_WK8 2.77 (0.50) 2.83 (0.49) 0.695
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯10 Pearson heatmap: TRBDD patients at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group) %>% 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)")
11 Pearson heatmap: TRBDD patients at week 8
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group) %>% 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)")
12 Pearson heatmap: Non-remitters at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df_remitters <- heatmap_df[which(heatmap_df$Remission=="Non-remitter"), ]
heatmap_df_remitters_bl<-heatmap_df_remitters %>% select(contains("BL")) %>% select(-log_IL1A_BL,-log_IL2_BL,-log_IFNG_BL) %>% tidyr::drop_na()
heatmap_df_remitters_bl<-as.matrix(sapply(heatmap_df_remitters_bl, as.numeric))
corr <- round(cor(heatmap_df_remitters_bl, use="pairwise.complete.obs"), 2)
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE, lab_size=1.5, insig="blank", tl.cex=5, title = "Pearson Correlation Matrix (Non-remitters at Baseline)")
13 Pearson heatmap: Remitters at baseline
list_vars<-combined_df %>% dplyr::select(-Sex, -Arm, -Response, -Subject_ID, -Group) %>% names()
heatmap_df<-combined_df %>% dplyr::select(all_of(list_vars))
heatmap_df_remitters <- heatmap_df[which(heatmap_df$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)")
14 Univariate screen by SIRI_BL
list_vars<-combined_df %>% dplyr::select(-Sex, -Remission, -Response, -Subject_ID, -Group) %>% 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 |
| 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 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)
16 MODEL 1: SIRI by HAMD17*Timepoint
17 MODEL 2: HAMD17_WK8 by SIRI_BL
# SII_model<-lm(HAMD17_WK8~Arm+HAMD17_BL+log(SII_BL), data=combined_df)
# sjPlot::tab_model(SII_model)
SIRI_model<-lm(HAMD17_WK8~Sex+Age+log_BMI+Arm+HAMD17_BL+log_SIRI_BL, data=combined_df)
sjPlot::tab_model(SIRI_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 | ||
18 MODEL 3: 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")
SIRI_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(SIRI_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 | ||
19 MODEL 4 (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+Age+Arm+HAMD17_BL*log_SIRI_BL, data=combined_df)
# plot(SII_model_interaction, which=c(2,6))
sjPlot::tab_model(SIRI_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 | ||
interactions::interact_plot(SIRI_model_interaction, pred = log_SIRI_BL, modx = Age, jitter=0.1, plot.points = TRUE, main.title = "Tx outcomes linked to baseline SIRI-to-Age interaction")