Demographics

Demographic information compared against two income groups using Wilcox for continuous and ordinal variables and Fischer's-exact test for categorical variables.

#Wilcox
wcAge<- wilcox.test(Age ~IncomeDi, data=CART15data, exact =FALSE)
wcEd<- wilcox.test(EducationLevelCoded ~IncomeDi, data=CART15data, exact =FALSE)
wcLines<- wilcox.test(lines ~IncomeDi, data=CART15data)
wcLDH<- wilcox.test(`LDH at Day 0` ~IncomeDi, data=CART15data)
wcCR<- wilcox.test(day28coded ~IncomeDi, data=CART15data)
wcCRSdays<- wilcox.test(CART15data$`Day to CRS`~IncomeDi, data=CART15data)
wcNTXdays<- wilcox.test(CART15data$`Day to NTX`~IncomeDi, data=CART15data)
wcCRSgrade<- wilcox.test(CART15data$`Max Grade CRS`~IncomeDi, data=CART15data)
wcNTXgrade<- wilcox.test(CART15data$`Max Grade NTX`~IncomeDi, data=CART15data)


#Fisher
ftSex<- fisher.test(CART15data$Sex,  CART15data$IncomeDi)
ftDx<- fisher.test(CART15data$dx, CART15data$IncomeDi)
ftAllo<- fisher.test(CART15data$`Allo (Y=1, N=0)`, CART15data$IncomeDi)
ftAuto<- fisher.test(CART15data$`Auto (Y=1, N=0)`, CART15data$IncomeDi)
ftCRS<- fisher.test(CART15data$`CRS (Y=1 /N =0)`, CART15data$IncomeDi)
ftNTX<- fisher.test(CART15data$`NTX (yes=1, no =0)`, CART15data$IncomeDi)

#p-values
data.frame(wcAge$p.value, ftSex$p.value, wcEd$p.value, wcLDH$p.value, wcLines$p.value, ftAllo$p.value, ftAuto$p.value, wcCR$p.value, ftCRS$p.value, wcCRSdays$p.value, wcCRSgrade$p.value, ftNTX$p.value, wcNTXgrade$p.value, wcNTXdays$p.value)
#Age Comparision Char
ggboxplot(CART15data, x = "IncomeDi", y = "Age", 
color = "IncomeDi", palette = c("#00AFBB", "#E7B800"),
ylab = "Age", xlab = "IncomeDi")

Cytokines

Peak Cytokine concentrations compared against two income groups using student t.test

ttBCA1<- t.test(LogBCA1 ~ IncomeDi, data=CART15data)
ttFrac<- t.test(LogFractalkine ~ IncomeDi, data=CART15data)
ttGCSF<- t.test(LogGCSF ~ IncomeDi, data=CART15data)
ttI309<- t.test(LogI309 ~ IncomeDi, data=CART15data)
ttIFNg<- t.test(LogIFNg ~ IncomeDi, data=CART15data)
ttIL6<- t.test(LogIL6 ~ IncomeDi, data=CART15data)
ttIL8<- t.test(LogIL8 ~ IncomeDi, data=CART15data)
ttIP10<- t.test(LogIP10 ~ IncomeDi, data=CART15data)
ttMCP2<- t.test(LogMCP2 ~ IncomeDi, data=CART15data)
ttTNFa<- t.test(LogTNFa ~ IncomeDi, data=CART15data)

data.frame(ttBCA1$p.value, ttFrac$p.value, ttGCSF$p.value, ttI309$p.value, ttIFNg$p.value, ttIL6$p.value, ttIL8$p.value, ttIP10$p.value, ttMCP2$p.value, ttTNFa$p.value)

Kynurenine Metabolites

Kynurenine metabolites compared against two income groups using linear mixed effects model and estimate marginal means to compare groups at each time point.

lmrTRP <- lmer(LogTRP~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emTRP<- emmeans(lmrTRP, pairwise~IncomeDiCode|TimeCode)

lmrKYN <- lmer(LogKYN~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emKYN<- emmeans(lmrKYN, pairwise~IncomeDiCode|TimeCode)

lmrKA <- lmer(LogKA~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emKA<- emmeans(lmrKA, pairwise~IncomeDiCode|TimeCode)

lmrHK <- lmer(LogHK~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emHK<- emmeans(lmrHK, pairwise~IncomeDiCode|TimeCode)

lmrHAA <- lmer(LogHAA2~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emHAA<- emmeans(lmrHAA, pairwise~IncomeDiCode|TimeCode)

lmrQA <- lmer(LogQA~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emQA<- emmeans(lmrQA, pairwise~IncomeDiCode|TimeCode)

emTRP$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low 0.140400 0.120 43.7 1.166   0.2500 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low 0.126703 0.129 45.0 0.979   0.3328 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low 0.000227 0.126 44.4 0.002   0.9986 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low 0.083203 0.129 44.9 0.643   0.5238 
## 
## Degrees-of-freedom method: kenward-roger
emKYN$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.1887 0.127 39.7 -1.486  0.1452 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.1121 0.136 42.2 -0.826  0.4136 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.1105 0.132 41.0 -0.836  0.4082 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.0895 0.136 42.0 -0.659  0.5135 
## 
## Degrees-of-freedom method: kenward-roger
emKA$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   0.1025 0.195 35.7  0.526  0.6022 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.0268 0.207 38.8 -0.129  0.8979 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.0756 0.202 37.4 -0.374  0.7108 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   0.1426 0.207 38.7  0.688  0.4958 
## 
## Degrees-of-freedom method: kenward-roger
emHK$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.3084 0.255 20.3 -1.210  0.2403 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.2809 0.263 22.3 -1.070  0.2961 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.3491 0.260 21.5 -1.344  0.1930 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low  -0.0291 0.263 22.4 -0.111  0.9129 
## 
## Degrees-of-freedom method: kenward-roger
emHAA$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.569 0.396 24.1 -1.437  0.1636 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.694 0.411 26.8 -1.686  0.1033 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.893 0.406 25.7 -2.202  0.0368 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.396 0.412 26.9 -0.961  0.3449 
## 
## Degrees-of-freedom method: kenward-roger
emQA$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE df t.ratio p.value
##  high - low   -0.733 0.211 14 -3.469  0.0038 
## 
## TimeCode = D14:
##  contrast   estimate    SE df t.ratio p.value
##  high - low   -0.920 0.265 14 -3.471  0.0037 
## 
## TimeCode = D28:
##  contrast   estimate    SE df t.ratio p.value
##  high - low   -0.662 0.247 14 -2.683  0.0178 
## 
## TimeCode = D90:
##  contrast   estimate    SE df t.ratio p.value
##  high - low   -0.468 0.294 14 -1.589  0.1345 
## 
## Degrees-of-freedom method: kenward-roger

PROs

Kynurenine metabolites compared against two income groups using linear mixed effects model and estimate marginal means to compare groups at each time point.

lmrDep <- lmer(Dep~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emDep<- emmeans(lmrDep, pairwise~IncomeDiCode|TimeCode)

lmrAnx <- lmer(Anx~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emAnx<- emmeans(lmrAnx, pairwise~IncomeDiCode|TimeCode)

lmrPSQI <- lmer(PSQI~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emPSQI<- emmeans(lmrPSQI, pairwise~IncomeDiCode|TimeCode)

lmrFSII <- lmer(FSII~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emFSII<- emmeans(lmrFSII, pairwise~IncomeDiCode|TimeCode)

lmrFSIF <- lmer(FSIF~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emFSIF<- emmeans(lmrFSIF, pairwise~IncomeDiCode|TimeCode)

lmrFSID <- lmer(FSID~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emFSID<- emmeans(lmrFSID, pairwise~IncomeDiCode|TimeCode)

lmrBPII <- lmer(BPII~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emBPII<- emmeans(lmrBPII, pairwise~IncomeDiCode|TimeCode)

lmrBPIF <- lmer(BPIF~IncomeDiCode*TimeCode+(1|ID), data=CARTdata)
emBPIF<- emmeans(lmrBPIF, pairwise~IncomeDiCode|TimeCode)

emDep$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.804 5.46 29.7 -0.147  0.8841 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    9.096 6.96 38.6  1.307  0.1990 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -3.718 5.80 32.4 -0.641  0.5258 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.836 6.22 35.4 -0.295  0.7696 
## 
## Degrees-of-freedom method: kenward-roger
emAnx$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.482 3.05 21.8 -0.158  0.8759 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    0.217 3.63 32.1  0.060  0.9527 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    0.985 3.18 24.3  0.310  0.7591 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.717 3.34 27.3 -0.515  0.6110 
## 
## Degrees-of-freedom method: kenward-roger
emPSQI$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    -2.54 1.75 31.8 -1.451  0.1564 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    -1.71 2.35 37.4 -0.727  0.4717 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    -1.68 1.96 34.7 -0.856  0.3977 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    -4.08 2.11 36.2 -1.931  0.0614 
## 
## Degrees-of-freedom method: kenward-roger
emFSII$contrasts
## TimeCode = Baseline:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -1.228 0.988 28.1 -1.243  0.2241 
## 
## TimeCode = D14:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low    1.350 1.244 37.8  1.085  0.2849 
## 
## TimeCode = D28:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.364 1.045 31.0 -0.349  0.7298 
## 
## TimeCode = D90:
##  contrast   estimate    SE   df t.ratio p.value
##  high - low   -0.785 1.117 34.1 -0.703  0.4871 
## 
## Degrees-of-freedom method: kenward-roger
emFSIF$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.796 1.03 34.6 -0.771  0.4460 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    1.389 1.35 40.2  1.025  0.3113 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.721 1.11 36.6 -1.557  0.1280 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.489 1.20 38.5 -0.408  0.6852 
## 
## Degrees-of-freedom method: kenward-roger
emFSID$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.750 2.52 27.7 -0.298  0.7681 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    7.223 3.16 37.6  2.283  0.0282 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.288 2.66 30.6 -0.484  0.6320 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    0.645 2.84 33.8  0.227  0.8220 
## 
## Degrees-of-freedom method: kenward-roger
emBPII$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.545 1.05 23.7 -1.472  0.1541 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low    0.797 1.27 34.4  0.626  0.5356 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.884 1.10 26.4 -1.715  0.0981 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -2.813 1.16 29.7 -2.424  0.0217 
## 
## Degrees-of-freedom method: kenward-roger
emBPIF$contrasts
## TimeCode = Baseline:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -2.204 1.21 24.1 -1.827  0.0802 
## 
## TimeCode = D14:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -0.488 1.47 34.8 -0.332  0.7422 
## 
## TimeCode = D28:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.973 1.26 26.9 -1.560  0.1306 
## 
## TimeCode = D90:
##  contrast   estimate   SE   df t.ratio p.value
##  high - low   -1.560 1.34 30.1 -1.166  0.2527 
## 
## Degrees-of-freedom method: kenward-roger

Checking Confounding Variables

LDH represents intial tumor burden and was correlated with SES groupings. It is important to understand if and how this relationship might affect SES impact on cytokines, kynureine metabolites, clinical outcomes, and PROs. Spearman ranked correlations and Chi-squared tests was done on demogrpahic data. ANOVA done on peak cytokines. Only baseline values were used on kynurenine metabolites and PROs across timepoints to run covariant ANOVA of LDH*income.

#Wilcox
pcAge<- cor.test(CART15data$Age, CART15data$`LDH at Day 0`, method=c('pearson'))
pcEd<- cor.test(CART15data$EducationLevelCoded, CART15data$`LDH at Day 0`, method=c('pearson'))
pclines<- cor.test(CART15data$lines, CART15data$`LDH at Day 0`, method=c('pearson'))
pcIncome<- cor.test(CART15data$IncomeDi, CART15data$`LDH at Day 0`, method=c('pearson'))
pcCR<- cor.test(CART15data$day28coded, CART15data$`LDH at Day 0`, method=c('pearson'))
pcCRSdays<- cor.test(CART15data$`Day to CRS`, CART15data$`LDH at Day 0`, method=c('pearson'))
pcNTXdays<- cor.test(CART15data$`Day to NTX`, CART15data$`LDH at Day 0`, method=c('pearson'))
pcCRSgrade<- cor.test(CART15data$`Max Grade CRS`, CART15data$`LDH at Day 0`, method=c('pearson'))
pcNTXgrade<- cor.test(CART15data$`Max Grade NTX`, CART15data$`LDH at Day 0`, method=c('pearson'))
pcCRS <-cor.test(CART15data$`CRS (Y=1 /N =0)`, CART15data$`LDH at Day 0`, method=c('pearson'))
pcNTX <-cor.test(CART15data$`NTX (yes=1, no =0)`, CART15data$`LDH at Day 0`, method=c('pearson'))


#Chi Squared
cqSex<-chisq.test(CART15data$Sex,  CART15data$`LDH at Day 0`)
cqDx<-chisq.test(CART15data$dx,  CART15data$`LDH at Day 0`)
cqAllo<-chisq.test(CART15data$`Allo (Y=1, N=0)`,  CART15data$`LDH at Day 0`)
cqAuto<-chisq.test(CART15data$`Auto (Y=1, N=0)`,  CART15data$`LDH at Day 0`)


#p-values
data.frame(pcAge$p.value, cqSex$p.value, pcEd$p.value, pcIncome$p.value, pclines$p.value, cqAllo$p.value, cqAuto$p.value, pcCR$p.value, pcCRS$p.value, pcCRSdays$p.value, pcCRSgrade$p.value, pcNTX$p.value, pcNTXgrade$p.value, pcNTXdays$p.value)
spBCA1<- cor.test(CART15data$LogBCA1, CART15data$`LDH at Day 0`)
spFrac<- cor.test(CART15data$LogFractalkine, CART15data$`LDH at Day 0`)
spGCSF<- cor.test(CART15data$LogGCSF, CART15data$`LDH at Day 0`)
spI309<- cor.test(CART15data$LogI309, CART15data$`LDH at Day 0`)
spIFNg<- cor.test(CART15data$LogIFNg, CART15data$`LDH at Day 0`)
spIL6<- cor.test(CART15data$LogIL6, CART15data$`LDH at Day 0`)
spIL8<- cor.test(CART15data$LogIL8, CART15data$`LDH at Day 0`)
spIP10<- cor.test(CART15data$LogIP10, CART15data$`LDH at Day 0`)
spMCP2<- cor.test(CART15data$LogMCP2, CART15data$`LDH at Day 0`)
spTNFa<- cor.test(CART15data$LogTNFa, CART15data$`LDH at Day 0`)

data.frame(spBCA1$p.value, spFrac$p.value, spGCSF$p.value, spI309$p.value, spIFNg$p.value, spIL6$p.value, spIL8$p.value, spIP10$p.value, spMCP2$p.value, spTNFa$p.value)
avBCA1<- aov(CART15data$LogBCA1 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avFrac<- aov(CART15data$LogFractalkine ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avGCSF<- aov(CART15data$LogGCSF ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avI309<- aov(CART15data$LogI309 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avIFNg<- aov(CART15data$LogIFNg ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avIL6<- aov(CART15data$LogIL6 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avIL8F<- aov(CART15data$LogIL8 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avIP10F<- aov(CART15data$LogIP10 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avMCP2<- aov(CART15data$LogMCP2 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avTNFa<- aov(CART15data$LogTNFa ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`) 
 
summary(avBCA1)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 1.0630  1.0630   4.187
## CART15data$`LDH at Day 0`                          1 0.5884  0.5884   2.317
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.6816  0.6816   2.684
## Residuals                                         11 2.7930  0.2539        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0654 .
## CART15data$`LDH at Day 0`                         0.1561  
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.1296  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avFrac)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  0.191  0.1907   0.370
## CART15data$`LDH at Day 0`                          1  0.622  0.6217   1.207
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  0.003  0.0028   0.006
## Residuals                                         11  5.666  0.5151        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.555
## CART15data$`LDH at Day 0`                          0.295
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.942
## Residuals
summary(avGCSF)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  2.396  2.3965   6.066
## CART15data$`LDH at Day 0`                          1  1.354  1.3537   3.427
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  0.237  0.2371   0.600
## Residuals                                         11  4.346  0.3950        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0315 *
## CART15data$`LDH at Day 0`                         0.0912 .
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.4548  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avI309)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 1.1243  1.1243   7.166
## CART15data$`LDH at Day 0`                          1 0.4965  0.4965   3.164
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0807  0.0807   0.514
## Residuals                                         11 1.7259  0.1569        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0215 *
## CART15data$`LDH at Day 0`                         0.1029  
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.4882  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avIFNg)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  1.393  1.3930   1.489
## CART15data$`LDH at Day 0`                          1  0.019  0.0188   0.020
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  0.012  0.0120   0.013
## Residuals                                         11 10.291  0.9355        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.248
## CART15data$`LDH at Day 0`                          0.890
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.912
## Residuals
summary(avIL6)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  1.834  1.8342   3.515
## CART15data$`LDH at Day 0`                          1  0.463  0.4630   0.887
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  0.102  0.1022   0.196
## Residuals                                         11  5.740  0.5218        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0876 .
## CART15data$`LDH at Day 0`                         0.3664  
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.6666  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avIL8F)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 1.2588  1.2588   6.593
## CART15data$`LDH at Day 0`                          1 0.4050  0.4050   2.121
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0437  0.0437   0.229
## Residuals                                         11 2.1003  0.1909        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0262 *
## CART15data$`LDH at Day 0`                         0.1732  
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.6419  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avIP10F)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 1.0452  1.0452   9.772
## CART15data$`LDH at Day 0`                          1 0.0004  0.0004   0.004
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.1094  0.1094   1.022
## Residuals                                         11 1.1766  0.1070        
##                                                    Pr(>F)   
## CART15data$IncomeDiCode                           0.00965 **
## CART15data$`LDH at Day 0`                         0.95136   
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.33367   
## Residuals                                                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avMCP2)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.6632  0.6632   5.254
## CART15data$`LDH at Day 0`                          1 0.0838  0.0838   0.664
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0388  0.0388   0.307
## Residuals                                         11 1.3885  0.1262        
##                                                   Pr(>F)  
## CART15data$IncomeDiCode                           0.0426 *
## CART15data$`LDH at Day 0`                         0.4324  
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.5905  
## Residuals                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(avTNFa)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 1.5452  1.5452  11.768
## CART15data$`LDH at Day 0`                          1 0.9383  0.9383   7.145
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0003  0.0003   0.002
## Residuals                                         11 1.4444  0.1313        
##                                                    Pr(>F)   
## CART15data$IncomeDiCode                           0.00562 **
## CART15data$`LDH at Day 0`                         0.02167 * 
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.96253   
## Residuals                                                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pcTrp<- cor.test(CARTdata$LogTRP, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcKyn <- cor.test(CARTdata$LogKYN, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcKA <- cor.test(CARTdata$LogKA, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcHK <- cor.test(CARTdata$LogHK, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcHAA <- cor.test(CARTdata$LogHAA2, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcQA <- cor.test(CARTdata$LogQA, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))

data.frame(pcTrp$p.value, pcKyn$p.value, pcKA$p.value, pcHK$p.value, pcHAA$p.value, pcQA$p.value)
avTRP<- aov(CART15data$LogTRP ~ CART15data$IncomeDiCode*CART15data$`LDH at Day 0`)
avKYN<- aov(CART15data$LogKYN ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avKA<- aov(CART15data$LogKA ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avHK<- aov(CART15data$LogHK ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avHAA<- aov(CART15data$LogHAA2 ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avQA<- aov(CART15data$LogQA ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
 
summary(avTRP)
##                                                   Df  Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.07359 0.07359   2.840
## CART15data$`LDH at Day 0`                          1 0.00095 0.00095   0.037
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.03674 0.03674   1.418
## Residuals                                         11 0.28506 0.02591        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.120
## CART15data$`LDH at Day 0`                          0.852
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.259
## Residuals
summary(avKYN)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.1329 0.13295   2.312
## CART15data$`LDH at Day 0`                          1 0.0395 0.03952   0.687
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0828 0.08283   1.441
## Residuals                                         11 0.6325 0.05750        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.157
## CART15data$`LDH at Day 0`                          0.425
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.255
## Residuals
summary(avKA)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.0393 0.03926   0.242
## CART15data$`LDH at Day 0`                          1 0.0015 0.00155   0.010
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0203 0.02033   0.125
## Residuals                                         11 1.7847 0.16224        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.632
## CART15data$`LDH at Day 0`                          0.924
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.730
## Residuals
summary(avHK)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.3551  0.3551   2.002
## CART15data$`LDH at Day 0`                          1 0.0004  0.0004   0.002
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.2443  0.2443   1.378
## Residuals                                         11 1.9504  0.1773        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.185
## CART15data$`LDH at Day 0`                          0.961
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.265
## Residuals
summary(avHAA)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  1.207  1.2068   1.709
## CART15data$`LDH at Day 0`                          1  0.992  0.9916   1.405
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  0.351  0.3506   0.497
## Residuals                                         11  7.765  0.7059        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.218
## CART15data$`LDH at Day 0`                          0.261
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.496
## Residuals
summary(avQA)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1 0.9199  0.9199  84.533
## CART15data$`LDH at Day 0`                          1 0.0103  0.0103   0.943
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1 0.0205  0.0205   1.888
## Residuals                                          3 0.0326  0.0109        
##                                                    Pr(>F)   
## CART15data$IncomeDiCode                           0.00272 **
## CART15data$`LDH at Day 0`                         0.40314   
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0` 0.26313   
## Residuals                                                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness
pcDep<- cor.test(CARTdata$Dep, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcAnx <- cor.test(CARTdata$Anx, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcPSQI <- cor.test(CARTdata$PSQI, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcBPII <- cor.test(CARTdata$BPII, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcBPIF <- cor.test(CARTdata$BPIF, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcFSII <- cor.test(CARTdata$FSII, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcFSID <- cor.test(CARTdata$FSID, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))
pcFSIF <- cor.test(CARTdata$FSIF, CARTdata$`LDH at Day 0` * CARTdata$Time, method =c('pearson'))

data.frame(pcDep$p.value, pcAnx$p.value, pcPSQI$p.value, pcBPII$p.value, pcBPIF$p.value, pcFSII$p.value, pcFSID$p.value, pcFSIF$p.value)
avDep<- aov(CART15data$Dep ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avAnx<- aov(CART15data$Anx ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avPSQI<- aov(CART15data$PSQI ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avBPII<- aov(CART15data$BPII ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avBPIF<- aov(CART15data$BPIF ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avFSII<- aov(CART15data$FSII ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avFSID<- aov(CART15data$FSID ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)
avFSIF<- aov(CART15data$FSIF ~ CART15data$IncomeDiCode * CART15data$`LDH at Day 0`)

summary(avDep)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1    2.4    2.41   0.031
## CART15data$`LDH at Day 0`                          1  238.9  238.94   3.112
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1    0.0    0.02   0.000
## Residuals                                         11  844.6   76.78        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.863
## CART15data$`LDH at Day 0`                          0.105
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.988
## Residuals
summary(avAnx)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1   0.87   0.868   0.058
## CART15data$`LDH at Day 0`                          1  24.57  24.575   1.645
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  13.87  13.869   0.929
## Residuals                                         11 164.29  14.935        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.814
## CART15data$`LDH at Day 0`                          0.226
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.356
## Residuals
summary(avPSQI)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  24.00  24.005   2.763
## CART15data$`LDH at Day 0`                          1  17.72  17.719   2.039
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1   5.63   5.627   0.648
## Residuals                                         11  95.58   8.689        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.125
## CART15data$`LDH at Day 0`                          0.181
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.438
## Residuals
summary(avBPII)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1   8.91   8.907   1.985
## CART15data$`LDH at Day 0`                          1   9.18   9.183   2.047
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1   0.79   0.789   0.176
## Residuals                                         11  49.35   4.487        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.186
## CART15data$`LDH at Day 0`                          0.180
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.683
## Residuals
summary(avBPIF)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1  18.14  18.136   2.690
## CART15data$`LDH at Day 0`                          1  20.36  20.355   3.019
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1   0.37   0.366   0.054
## Residuals                                         11  74.17   6.743        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.129
## CART15data$`LDH at Day 0`                          0.110
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.820
## Residuals
summary(avFSII)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1   5.63   5.627   1.745
## CART15data$`LDH at Day 0`                          1   6.39   6.392   1.982
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1   0.09   0.089   0.028
## Residuals                                         11  35.48   3.225        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.213
## CART15data$`LDH at Day 0`                          0.187
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.871
## Residuals
summary(avFSID)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1   2.10    2.10   0.100
## CART15data$`LDH at Day 0`                          1   0.08    0.08   0.004
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1  14.23   14.23   0.677
## Residuals                                         11 231.19   21.02        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.758
## CART15data$`LDH at Day 0`                          0.952
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.428
## Residuals
summary(avFSIF)
##                                                   Df Sum Sq Mean Sq F value
## CART15data$IncomeDiCode                            1   2.37   2.365   0.667
## CART15data$`LDH at Day 0`                          1   7.67   7.666   2.161
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  1   0.36   0.360   0.101
## Residuals                                         11  39.03   3.548        
##                                                   Pr(>F)
## CART15data$IncomeDiCode                            0.432
## CART15data$`LDH at Day 0`                          0.170
## CART15data$IncomeDiCode:CART15data$`LDH at Day 0`  0.756
## Residuals