.Rhistory

#time to set up model and then evaluate
compMAR.mod <- glm(PCHGCA1N ~ SEXN + TRTPN, family=binomial, data=compMAR.dat)
compMAR.dat$phat.cmpl  <- compMAR.mod$fitted.values
summary(compMAR.mod)
#check predictive power
roc(compMAR.dat$PCHGCA1N, fitted(compMAR.mod), plot=TRUE, legacy.axes=TRUE, print.auc=TRUE)
#pretty decent AUC value = C-statistic -- decent predictive power
#so with an AUC of .712, we have somewhat acceptable discrimination
#let's look at deviance residuals
devres <- residuals(compMAR.mod,"deviance")
plot(compMAR.dat$phat.cmpl, devres, main = "Deviance Residual Plot", xlab= "Estimated Probability", ylab="Deviance Residuals")
abline(h=c(-2,2))
#many repeat cases? so should look at another measure to evaluate model fit
#we have quite a few repeat cases as we only have a few levels so we can use the
#Deviance GoF test to check calibration
#check model fit and assess calibration
devdif.po <- compMAR.mod$null.deviance - compMAR.mod$deviance
df.po <- 3 - 1 #p1-p0 = (2+1)-(0+1)
pval.po <- pchisq(devdif.po, df.po, ncp=0, lower.tail = FALSE, log.p = FALSE)
cbind(devdif.po,df.po,pval.po)
#we reject null and conclude that the model does NOT fit the data well
#logistic response function is NOT appropriate for this dataset
#IMPUTED AS NON-RESPONDER
#create imputed dataset
impMAR.dat <- MARdata.respdat
i <- 1
for(i in 1:length(impMAR.dat$USUBJID)){
if(is.na(MARdata.respdat$AVAL[i])==TRUE){
impMAR.dat$PCHGCA1N[i] <- 0
i <- i+1
}
}
#time to set up model and then evaluate
impMAR.mod <- glm(PCHGCA1N ~ SEXN + TRTPN, family=binomial, data=impMAR.dat)
impMAR.dat$phat.imp  <- impMAR.mod$fitted.values
summary(impMAR.mod)
#check predictive power
roc(impMAR.dat$PCHGCA1N, fitted(impMAR.mod), plot=TRUE, legacy.axes=TRUE, print.auc=TRUE)
#pretty decent AUC value = C-statistic -- decent predictive power
#so with an AUC of .721, we have acceptable discrimination -- BETTER THAN COMPLETER
#let's look at deviance residuals
devres <- residuals(impMAR.mod,"deviance")
plot(impMAR.dat$phat.imp, devres, main = "Deviance Residual Plot", xlab= "Estimated Probability", ylab="Deviance Residuals")
abline(h=c(-2,2))
#many repeat cases? so should look at another measure to evaluate model fit
#we have quite a few repeat cases as we only have a few levels so we can use the
#Deviance GoF test to check calibration
#check model fit and assess calibration
devdif.po <- impMAR.mod$null.deviance - impMAR.mod$deviance
df.po <- 3 - 1 #p1-p0 = (2+1)-(0+1)
pval.po <- pchisq(devdif.po, df.po, ncp=0, lower.tail = FALSE, log.p = FALSE)
cbind(devdif.po,df.po,pval.po)
#we reject null and conclude that the model does NOT fit the data well
#logistic response function is NOT appropriate for this dataset
#to get a feel for how the predicted probabilities look based on the model
library(ggplot2)
ggplot() +
geom_freqpoly(data=ogADPAS.respdat, aes(x=phat.comp), color="chartreuse") +
geom_freqpoly(data=compMCAR10.dat, aes(x=phat.10cmpl), color="coral") +
geom_freqpoly(data=impMCAR10.dat, aes(x=phat.10imp), color="darkorchid") +
theme_classic()
ggplot() +
geom_point(data=ogADPAS.respdat, aes(x=phat.comp, y=TRTPN, shape=SEXN), color="chartreuse") +
geom_point(data=compMCAR10.dat, aes(x=phat.10cmpl, y=TRTPN, shape=SEXN), color="coral") +
geom_point(data=impMCAR10.dat, aes(x=phat.10imp, y=TRTPN, shape=SEXN), color="darkorchid") +
theme_classic()
ggplot() +
geom_line(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, group=SEXN, linetype=SEXN), color="chartreuse") + #whole data
geom_line(data=compMCAR10.dat, aes(x=TRTPN, y=phat.10cmpl, group=SEXN, linetype=SEXN), color="coral") + #completer data
geom_line(data=impMCAR10.dat, aes(x=TRTPN, y=phat.10imp, group=SEXN, linetype=SEXN), color="darkorchid") + #imputed as non-responder data
theme_classic()
library(ggplot2)
ggplot()+
scale_fill_brewer(palette="Dark2")+
geom_bar(data=ogADPAS.respdat, aes(x=TRTPN, fill=SEXN), position="dodge", color="black") +
geom_bar(data=compMCAR10.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "limegreen") +
#geom_bar(data=impMCAR10.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "limegreen") +
geom_bar(data=compMCAR20.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "blue") +
#geom_bar(data=impMCAR20.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "blue") +
geom_bar(data=compMCAR30.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "mediumorchid") +
#geom_bar(data=impMCAR30.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "mediumorchid") +
geom_bar(data=compMAR.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "red") +
#geom_bar(data=impMAR.dat, aes(x=TRTPN, fill=SEXN), position = "dodge", color = "red")
theme_classic()
ggplot() +
geom_freqpoly(data=ogADPAS.respdat, aes(x=TRTPN, color = SEXN), stat="count") +
geom_freqpoly(data=compMCAR10.dat, aes(x=phat.10cmpl), color="coral") +
geom_freqpoly(data=impMCAR10.dat, aes(x=phat.10imp), color="darkorchid") +
theme_classic()
#FEMALE GRAPH
ggplot() +
scale_fill_brewer(palette="Set2")+
geom_bar(data=subset(ogADPAS.respdat, ogADPAS.respdat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N), position="dodge",  color="black") + #whole data
geom_bar(data=subset(compMAR.dat, compMAR.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N), position="dodge",  color="red") + #completer data
geom_bar(data=subset(impMAR.dat, impMAR.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N),  position="dodge", color="red", linetype="dotted") + #imputed as non-responder data
geom_bar(data=subset(compMCAR10.dat, compMCAR10.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N),  position="dodge", color="limegreen") + #completer data
geom_bar(data=subset(impMCAR10.dat, impMCAR10.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N),  position="dodge", color="limegreen", linetype="dotted") + #imputed as non-responder data
geom_bar(data=subset(compMCAR20.dat, compMCAR20.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N), position="dodge",  color="blue") + #completer data
geom_bar(data=subset(impMCAR20.dat, impMCAR20.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N),  position="dodge", color="blue", linetype="dotted") + #imputed as non-responder data
geom_bar(data=subset(compMCAR30.dat, compMCAR30.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N), position="dodge",  color="mediumorchid") + #completer data
geom_bar(data=subset(impMCAR30.dat, impMCAR30.dat$SEXN == 2), aes(x=TRTPN, fill=PCHGCA1N), position="dodge",  color="mediumorchid", linetype="dotted") + #imputed as non-responder data
theme_classic()
tab1 <- as.data.frame(table(ogADPAS.respdat$PCHGCA1N, ogADPAS.respdat$TRTPN, ogADPAS.respdat$SEXN))
#PCHGCA1N: 0, non-responder; 1, responder TRTPN: 1, placebo; 2 active control (ustek);
#3, 140mg test; 4, 210 test SEXN: 1, male; 2, female, 3, unknown (no 3s appear)
tab2 <- as.data.frame(table(compMCAR10.dat$PCHGCA1N, compMCAR10.dat$TRTPN, compMCAR10.dat$SEXN))
tab3 <- as.data.frame(table(impMCAR10.dat$PCHGCA1N, impMCAR10.dat$TRTPN, impMCAR10.dat$SEXN))
tab4 <- as.data.frame(table(compMCAR20.dat$PCHGCA1N, compMCAR20.dat$TRTPN, compMCAR20.dat$SEXN))
tab5 <- as.data.frame(table(impMCAR20.dat$PCHGCA1N, impMCAR20.dat$TRTPN, impMCAR20.dat$SEXN))
tab6 <- as.data.frame(table(compMCAR30.dat$PCHGCA1N, compMCAR30.dat$TRTPN, compMCAR30.dat$SEXN))
tab7 <- as.data.frame(table(impMCAR30.dat$PCHGCA1N, impMCAR30.dat$TRTPN, impMCAR30.dat$SEXN))
tab8 <- as.data.frame(table(compMAR.dat$PCHGCA1N, compMAR.dat$TRTPN, compMAR.dat$SEXN))
tab9 <- (table(impMAR.dat$PCHGCA1N, impMAR.dat$TRTPN, impMAR.dat$SEXN))
plot(tab1) #will figure out graphs later
#FEMALE GRAPH
ggplot() +
geom_line(data=subset(ogADPAS.respdat, ogADPAS.respdat$SEXN == 2), aes(x=TRTPN, y=phat.comp, group=1), color="black") + #whole data
geom_line(data=subset(compMAR.dat, compMAR.dat$SEXN == 2), aes(x=TRTPN, y=phat.cmpl, group=1), color="red") + #completer data
geom_line(data=subset(impMAR.dat, impMAR.dat$SEXN == 2), aes(x=TRTPN, y=phat.imp, group=1), color="red", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR10.dat, compMCAR10.dat$SEXN == 2), aes(x=TRTPN, y=phat.10cmpl, group=1), color="limegreen") + #completer data
geom_line(data=subset(impMCAR10.dat, impMCAR10.dat$SEXN == 2), aes(x=TRTPN, y=phat.10imp, group=1), color="limegreen", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR20.dat, compMCAR20.dat$SEXN == 2), aes(x=TRTPN, y=phat.20cmpl, group=1), color="blue") + #completer data
geom_line(data=subset(impMCAR20.dat, impMCAR20.dat$SEXN == 2), aes(x=TRTPN, y=phat.20imp, group=1), color="blue", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR30.dat, compMCAR30.dat$SEXN == 2), aes(x=TRTPN, y=phat.30cmpl, group=1), color="mediumorchid") + #completer data
geom_line(data=subset(impMCAR30.dat, impMCAR30.dat$SEXN == 2), aes(x=TRTPN, y=phat.30imp, group=1), color="mediumorchid", linetype="dotted") + #imputed as non-responder data
labs(title = "Predicted Probability of Response for Females", x="Treatment Group", y = "Predicted Probability")+
scale_x_discrete(labels=c("1"="Placebo", "2"="Active Control", "3"= "140mg Drug X", "4"= "210mg Drug X")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#MALE GRAPH
ggplot() +
geom_line(data=subset(ogADPAS.respdat, ogADPAS.respdat$SEXN == 1), aes(x=TRTPN, y=phat.comp, group=1), color="black") + #whole data
geom_line(data=subset(compMAR.dat, compMAR.dat$SEXN == 1), aes(x=TRTPN, y=phat.cmpl, group=1), color="red") + #completer data
geom_line(data=subset(impMAR.dat, impMAR.dat$SEXN == 1), aes(x=TRTPN, y=phat.imp, group=1), color="red", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR10.dat, compMCAR10.dat$SEXN == 1), aes(x=TRTPN, y=phat.10cmpl, group=1), color="limegreen") + #completer data
geom_line(data=subset(impMCAR10.dat, impMCAR10.dat$SEXN == 1), aes(x=TRTPN, y=phat.10imp, group=1), color="limegreen", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR20.dat, compMCAR20.dat$SEXN == 1), aes(x=TRTPN, y=phat.20cmpl, group=1), color="blue") + #completer data
geom_line(data=subset(impMCAR20.dat, impMCAR20.dat$SEXN == 1), aes(x=TRTPN, y=phat.20imp, group=1), color="blue", linetype="dotted") + #imputed as non-responder data
geom_line(data=subset(compMCAR30.dat, compMCAR30.dat$SEXN == 1), aes(x=TRTPN, y=phat.30cmpl, group=1), color="mediumorchid") + #completer data
geom_line(data=subset(impMCAR30.dat, impMCAR30.dat$SEXN == 1), aes(x=TRTPN, y=phat.30imp, group=1), color="mediumorchid", linetype="dotted") + #imputed as non-responder data
labs(title = "Predicted Probability of Response for Males", x="Treatment Group", y = "Predicted Probability")+
scale_x_discrete(labels=c("1"="Placebo", "2"="Active Control", "3"= "140mg Drug X", "4"= "210mg Drug X")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#ALL INFORMATION
ggplot() +
geom_line(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, group=1), color="black") + #whole data
geom_point(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, shape=SEXN), color="black", size=1) +
geom_line(data=compMAR.dat, aes(x=TRTPN, y=phat.cmpl, group=1), color="red") + #completer data
geom_point(data=compMAR.dat, aes(x=TRTPN, y=phat.cmpl, shape=SEXN), color="red", size=1) +
geom_line(data=impMAR.dat, aes(x=TRTPN, y=phat.imp, group=1), color="red", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMAR.dat, aes(x=TRTPN, y=phat.imp, shape=SEXN), color="red", size=1) +
geom_line(data=compMCAR10.dat, aes(x=TRTPN, y=phat.10cmpl, group=1), color="limegreen") + #completer data
geom_point(data=compMCAR10.dat, aes(x=TRTPN, y=phat.10cmpl, shape=SEXN), color="limegreen", size=1) +
geom_line(data=impMCAR10.dat, aes(x=TRTPN, y=phat.10imp, group=1), color="limegreen", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR10.dat, aes(x=TRTPN, y=phat.10imp, shape=SEXN), color="limegreen", size=1) +
geom_line(data=compMCAR20.dat, aes(x=TRTPN, y=phat.20cmpl, group=1), color="blue") + #completer data
geom_point(data=compMCAR20.dat, aes(x=TRTPN, y=phat.20cmpl, shape=SEXN), color="blue", size=1) +
geom_line(data=impMCAR20.dat, aes(x=TRTPN, y=phat.20imp, group=1), color="blue", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR20.dat, aes(x=TRTPN, y=phat.20imp, shape=SEXN), color="blue", size=1) +
geom_line(data=compMCAR30.dat, aes(x=TRTPN, y=phat.30cmpl, group=1), color="mediumorchid") + #completer data
geom_point(data=compMCAR30.dat, aes(x=TRTPN, y=phat.30cmpl, shape=SEXN), color="mediumorchid", size=1) +
geom_line(data=impMCAR30.dat, aes(x=TRTPN, y=phat.30imp, group=1), color="mediumorchid", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR30.dat, aes(x=TRTPN, y=phat.30imp, shape=SEXN), color="mediumorchid", size=1) +
labs(title = "Predicted Probability of Response by Sex", x="Treatment Group", y = "Predicted Probability", shape = "Sex")+
scale_x_discrete(labels=c("1"="Placebo", "2"="Active Control", "3"= "140mg Drug X", "4"= "210mg Drug X")) +
scale_shape_discrete(labels=c("1"="Male", "2"="Female")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#COMPLETERS
ggplot() +
geom_line(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, group=1), color="black") + #whole data
geom_point(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, shape=SEXN), color="black") +
geom_line(data=compMAR.dat, aes(x=TRTPN, y=phat.cmpl, group=1), color="red") + #completer data
geom_point(data=compMAR.dat, aes(x=TRTPN, y=phat.cmpl, shape=SEXN), color="red") +
geom_line(data=compMCAR10.dat, aes(x=TRTPN, y=phat.10cmpl, group=1), color="limegreen") + #completer data
geom_point(data=compMCAR10.dat, aes(x=TRTPN, y=phat.10cmpl, shape=SEXN), color="limegreen") +
geom_line(data=compMCAR20.dat, aes(x=TRTPN, y=phat.20cmpl, group=1), color="blue") + #completer data
geom_point(data=compMCAR20.dat, aes(x=TRTPN, y=phat.20cmpl, shape=SEXN), color="blue") +
geom_line(data=compMCAR30.dat, aes(x=TRTPN, y=phat.30cmpl, group=1), color="mediumorchid") + #completer data
geom_point(data=compMCAR30.dat, aes(x=TRTPN, y=phat.30cmpl, shape=SEXN), color="mediumorchid") +
labs(title = "Predicted Probability of Response for Completer Data by Sex", x="Treatment Group", y = "Predicted Probability", shape="Sex")+
scale_x_discrete(labels=c("1"="Placebo", "2"="Active Control", "3"= "140mg Drug X", "4"= "210mg Drug X")) +
scale_shape_discrete(labels=c("1"="Male", "2"="Female")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#IMPUTED AS NON-RESP
ggplot() +
geom_line(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, group=1), color="black") + #whole data
geom_point(data=ogADPAS.respdat, aes(x=TRTPN, y=phat.comp, shape=SEXN), color="black") +
geom_line(data=impMAR.dat, aes(x=TRTPN, y=phat.imp, group=1), color="red", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMAR.dat, aes(x=TRTPN, y=phat.imp, shape=SEXN), color="red") +
geom_line(data=impMCAR10.dat, aes(x=TRTPN, y=phat.10imp, group=1), color="limegreen", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR10.dat, aes(x=TRTPN, y=phat.10imp, shape=SEXN), color="limegreen") +
geom_line(data=impMCAR20.dat, aes(x=TRTPN, y=phat.20imp, group=1), color="blue", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR20.dat, aes(x=TRTPN, y=phat.20imp, shape=SEXN), color="blue") +
geom_line(data=impMCAR30.dat, aes(x=TRTPN, y=phat.30imp, group=1), color="mediumorchid", linetype="dotted") + #imputed as non-responder data
geom_point(data=impMCAR30.dat, aes(x=TRTPN, y=phat.30imp, shape=SEXN), color="mediumorchid") +
labs(title = "Predicted Probability of Response for Imputed Data by Sex", x="Treatment Group", y = "Predicted Probability", shape="Sex")+
scale_x_discrete(labels=c("1"="Placebo", "2"="Active Control", "3"= "140mg Drug X", "4"= "210mg Drug X")) +
scale_shape_discrete(labels=c("1"="Male", "2"="Female")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#ODDS RATIOS and confidence intervals
zscore <- qnorm(.98)
ogsum <- summary(OGdata.mod)
c10sum <- summary(compMCAR10.mod)
i10sum <- summary(impMCAR10.mod)
c20sum <- summary(compMCAR20.mod)
i20sum <- summary(impMCAR20.mod)
c30sum <- summary(compMCAR30.mod)
i30sum <- summary(impMCAR30.mod)
cMARsum <- summary(compMAR.mod)
iMARsum <- summary(impMAR.mod)
#create rows of data: estimate, std error
FullData <- c(ogsum$coefficients[,1], ogsum$coefficients[,2])
c10MCAR <- c(c10sum$coefficients[,1], c10sum$coefficients[,2])
i10MCAR <- c(i10sum$coefficients[,1], i10sum$coefficients[,2])
c20MCAR <- c(c20sum$coefficients[,1], c20sum$coefficients[,2])
i20MCAR <- c(i20sum$coefficients[,1], i20sum$coefficients[,2])
c30MCAR <- c(c30sum$coefficients[,1], c30sum$coefficients[,2])
i30MCAR <- c(i30sum$coefficients[,1], i30sum$coefficients[,2])
cMAR <- c(cMARsum$coefficients[,1], cMARsum$coefficients[,2])
iMAR <- c(iMARsum$coefficients[,1], iMARsum$coefficients[,2])
datanamesN <- c(1,2,3,4,5,6,7,8,9)
table <- rbind(FullData, c10MCAR, i10MCAR, c20MCAR, i20MCAR, c30MCAR, i30MCAR, cMAR, iMAR)
table <- cbind(datanamesN, table)
table <- as.data.frame(table)
#where Lo=140mg, Hi=210mg
names(table) <- c("dataset", "intEst", "femEst", "acEst", "LoEst", "HiEst","intSE", "femSE", "acSE", "LoSE", "HiSE")
table$zscore <- rep(zscore,9)
#Female vs Male OR
ggplot(table, aes(x=exp(femEst), y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=exp(femEst + (zscore*femSE)), xmin = exp(femEst - (zscore*femSE)))) +
labs(title = "Female vs Male OR Estimates", x="OR", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Active Control vs Placebo OR
ggplot(table, aes(x=exp(acEst), y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=exp(acEst + (zscore*acSE)), xmin = exp(acEst - (zscore*acSE))))+
labs(title = "Active vs Placebo OR Estimates", x="OR", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#140mg vs Placebo OR
ggplot(table, aes(x=exp(LoEst), y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=exp(LoEst + (zscore*LoSE)), xmin = exp(LoEst - (zscore*LoSE))))+
labs(title = "140mg vs Placebo OR Estimates", x="OR", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#210mg vs Placebo OR
ggplot(table, aes(x=exp(HiEst), y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=exp(HiEst + (zscore*HiSE)), xmin = exp(HiEst - (zscore*HiSE))))+
labs(title = "210mg vs Placebo OR Estimates", x="OR", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
library(ggplot2)
#first: intercept
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Intercept Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
table(ogADPAS.respdat$PCHGCA1N, ogADPAS.respdat$TRTPN, ogADPAS.respdat$SEXN)
#PCHGCA1N: 0, non-responder; 1, responder TRTPN: 1, placebo; 2 active control (ustek);
#3, 140mg test; 4, 210 test SEXN: 1, male; 2, female, 3, unknown (no 3s appear)
table(compMCAR10.dat$PCHGCA1N, compMCAR10.dat$TRTPN, compMCAR10.dat$SEXN)
table(impMCAR10.dat$PCHGCA1N, impMCAR10.dat$TRTPN, impMCAR10.dat$SEXN)
library(ggplot2)
#first: intercept, b0
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
labs(title = "Active Control Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#140mg b4
ggplot(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
labs(title = "140mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#210mg b5
ggplot(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))+
labs(title = "210mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))+
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
labs(title = "Active Control Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
ggplot(data=table) +
geom_point(aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
ggplot(data=table) +
geom_point(aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE)))
ggplot() +
geom_point(table, aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE)))
library(ggplot2)
#first: intercept, b0
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))+
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset)))
library(ggplot2)
#first: intercept, b0
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset)))
geom_point() +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
labs(title = "Active Control Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
library(ggplot2)
#first: intercept, b0
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
labs(title = "Active Control Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#140mg b4
ggplot(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
labs(title = "140mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#210mg b5
ggplot(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))+
labs(title = "210mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
ggplot() +
geom_point(table, aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
geom_point(table, aes(x=femEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
geom_point(table, aes(x=acEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
geom_point(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
geom_point(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))+
ggplot() +
geom_point(table, aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
geom_point(table, aes(x=femEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
geom_point(table, aes(x=acEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
geom_point(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
geom_point(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_errorbarh(aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))
ggplot() +
geom_point(table, aes(x=intEst, y=as.factor(dataset))) +
geom_errorbarh(table, aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
geom_point(table, aes(x=femEst, y=as.factor(dataset))) +
geom_errorbarh(table,aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
geom_point(table, aes(x=acEst, y=as.factor(dataset))) +
geom_errorbarh(table,aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
geom_point(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_errorbarh(table,aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
geom_point(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_errorbarh(table,aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))
library(ggplot2)
#first: intercept, b0
ggplot(table, aes(x=intEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=intEst + (zscore*intSE), xmin = intEst - (zscore*intSE))) +
labs(title = "Intercept Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Female b2
ggplot(table, aes(x=femEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=femEst + (zscore*femSE), xmin = femEst - (zscore*femSE))) +
labs(title = "Female Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#Active Control b3
ggplot(table, aes(x=acEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=acEst + (zscore*acSE), xmin = acEst - (zscore*acSE)))+
labs(title = "Active Control Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#140mg b4
ggplot(table, aes(x=LoEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=LoEst + (zscore*LoSE), xmin = LoEst - (zscore*LoSE)))+
labs(title = "140mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
#210mg b5
ggplot(table, aes(x=HiEst, y=as.factor(dataset))) +
geom_point() +
geom_errorbarh(aes(xmax=HiEst + (zscore*HiSE), xmin = HiEst - (zscore*HiSE)))+
labs(title = "210mg Drug X Coefficient Estimates", x="Estimate Value", y = "Model")+
scale_y_discrete(labels=c("Full Data", "C MCAR10", "I MCAR10", "C MCAR20", "I MCAR20", "C MCAR30", "I MCAR30", "C MAR", "I MAR")) +
theme_classic() +
theme(plot.title=element_text(hjust=0.5))
table.flip <- cbind(FullData, c10MCAR, i10MCAR, c20MCAR, i20MCAR, c30MCAR, i30MCAR, cMAR, iMAR)
table.flip
table.flip <- cbind(FullData, c10MCAR, i10MCAR, c20MCAR, i20MCAR, c30MCAR, i30MCAR, cMAR, iMAR)
table.flip <- cbind(datanamesN, table.flip)
table.flip <- as.data.frame(table.flip)
table.flip
table.flip <- cbind(FullData, c10MCAR, i10MCAR, c20MCAR, i20MCAR, c30MCAR, i30MCAR, cMAR, iMAR)
table.flip <- rbind(datanamesN, table.flip)
table.flip <- as.data.frame(table.flip)
table.flip