R/4split_Placebo_combined.R
In nicksun1/Biomarker-Analysis: MMRM Analysis of New Biomarker
Defines functions
split_summary
# Biomarker Regression Analysis
#
# Log transformed analysis of combined biomarker data from studies A, CF, I, G
# Separates combined data in 4 different summary sets based on baseline biomarker value.
# Outputs csv file that includes all data split by thresholds where results of interest include %Change vs Placebo and Change in log(biomarker) vs Placebo
# Also outputs separate analysis and summary files for each individual threshold in format similar to individual study analysis output (studycf).
#
# Fit to model Change in log(biomarker) = Treatment and Fit to model log(y) = log(y_b) + Treatment
##
library(dplyr)
library(zoo)
library(EnvStats)
library(reshape)
suppressMessages(library(coastr))
## studycf
lab <- read.csv("labscf.csv")
studycf <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studycf <- studycf %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studycf %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter( VISID =="9") ###################
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[complete.cases(trt_merge[,14]),]
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studycf_comb <- trt_merge
studycf_comb$SUBJID <- paste("studycf",studycf_comb$SUBJID, sep="_")
## studya
lab <- read.csv("labsa.csv")
studya <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studya <- studya %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studya %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter(VISID =="10")
#trt_merge <- merge(studya, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studya_comb <- trt_merge
studya_comb$SUBJID <- paste("studya",studya_comb$SUBJID, sep="_")
## studyg
lab <- read.csv("labsg.csv")
adsl <- read.csv("adslg.csv")
studyg <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyg <- studyg %>% filter(PARAMCD =="ALBCS49C")
studyg <- studyg %>% filter(SAFFL=="Y")
studyg <- studyg %>% filter(AVISITN =="9")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyg, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyg_comb <- trt_merge
## studyi
lab <- read.csv("labsi.csv")
adsl <- read.csv("adsli.csv")
studyi <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyi <- studyi %>% filter(PARAMCD =="ALBCS49C")
studyi <- studyi %>% filter(SAFFL=="Y")
studyi <- studyi %>% filter(AVISITN =="204")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyi, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyi_comb <- trt_merge
#### Combine studies and Filter by biomarker value
combined <- merge(studycf_comb, studya_comb, all=TRUE)
combined <- merge(combined, studyi_comb, all=TRUE)
combined <- merge(combined, studyg_comb, all=TRUE)
ten <- combined %>% filter (base_unchanged<10)
thirty <- combined %>% filter (base_unchanged>=10 & base_unchanged<30)
threehundred <- combined %>% filter (base_unchanged>=30 & base_unchanged<300)
overhundred <- combined %>% filter (base_unchanged>=300)
ten$Split <- "<10"
thirty$Split<- ">=10 and <30"
threehundred$Split<- ">=30 and <300"
overhundred$Split<- ">=300"
num_patients <- merge(ten,thirty,all=TRUE)
num_patients <- merge(num_patients,threehundred,all=TRUE)
num_patients <- merge(num_patients,overhundred,all=TRUE)
num <- num_patients %>% group_by(TRT,Split) %>% summarise( n=n() )
combined_above <- overhundred
hold_percentchange <- combined_above %>% filter(VISID=="9" | VISID=="10" | VISID=="204")
hold_percentchange$Change[hold_percentchange$Change==0]<- NA
hold_percentchange <- hold_percentchange[complete.cases(hold_percentchange),]
percent_change_sum<- hold_percentchange %>% group_by(TRT) %>% summarise( geomean= (exp(mean(Percent_change3))-1)*100 ,
geoSE =( exp(mean(Percent_change3))*100*(sd(Percent_change3)/sqrt(n()))),n=n() )
percent_change_sum$delta <- percent_change_sum$geomean-percent_change_sum$geomean[percent_change_sum$TRT=="Placebo"]
percent_change_sum$deltaSE <- sqrt((percent_change_sum$geoSE[percent_change_sum$TRT=="Placebo"])^2+(percent_change_sum$geoSE)^2)
percent_change_sum$VISID <- "%Change vs Placebo"
percent_change_sum<-percent_change_sum[1:2,c(1,7,5,6)]
names(percent_change_sum)<-c("TRT","VISID","geomean","geoSE")
percent_change_sum$Lower <- percent_change_sum$geomean-qnorm(0.975)*percent_change_sum$geoSE
percent_change_sum$Upper <- percent_change_sum$geomean+qnorm(0.975)*percent_change_sum$geoSE
combined_above$TRT<-as.factor(combined_above$TRT)
combined_above$TRT = relevel(combined_above$TRT, ref="Placebo")
lm_data <- combined_above
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:3,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:3,1:2])
ci1$TRT <- "Drug_0.75"
ci1[2,3] <-"Drug_1.5"
ci1$VISID <-"Change in log(biomarker) vs Placebo"
names(ci1) <-c("Lower", "Upper","TRT","VISID")
trt_diff_merge <-merge(trt_diff,ci1,all=TRUE)
final_diff <- merge(percent_change_sum, trt_diff_merge,all=TRUE)
final_diff$Threshold <- "<10"
final_diff<- final_diff[,c(7,1,2,3,4,5,6)]
names(final_diff) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
final_diff_max <- final_diff
final_diff_30$Threshold <- ">=10 and <30"
final_diff_300$Threshold <- ">=30 and <300"
final_diff_max$Threshold <- ">=300"
final_merge<-merge(final_diff_10,final_diff_30,all=TRUE)
final_merge<-merge(final_merge,final_diff_300,all=TRUE)
final_merge<-merge(final_merge,final_diff_max,all=TRUE)
write.csv(final_merge, "Placebo_comparison_4_thresholds.csv")
#### Summary function
split_summary <-function( data, split){
data$VISID<-"Last Visit"
visit_sum_last <- data %>% group_by(TRT,VISID,Split) %>% summarise( mean= mean(AVAL),n=n(), sd =sd(AVAL), geomean =geoMean(aval_unchanged), geoSE= geoSD(aval_unchanged))
visit_sum_base <- data %>% group_by(TRT,VISID,Split) %>% summarise( mean= mean(BASE),n=n(), sd =sd(BASE), geomean =geoMean(base_unchanged), geoSE= geoSD(base_unchanged))
visit_sum_base$VISID <- "Baseline"
visit_sum <- merge(visit_sum_last, visit_sum_base,all=TRUE)
temp3 <- data
temp3$Change[temp3$Change==0]<- NA
temp3 <- temp3[complete.cases(temp3),]
hold2 <- temp3 %>% group_by(TRT, VISID,Split) %>% summarise( mean= mean(log(aval_unchanged)-log(base_unchanged)), n= n(), sd =(sd(log(aval_unchanged)-log(base_unchanged))),
geoSE =(sd(log(aval_unchanged)-log(base_unchanged))/sqrt(n())))
change_sum <- hold2
change_sum$VISID ="Change"
percent_change_sum2<- temp3 %>% group_by(TRT, VISID, Split) %>% summarise( geomean= (exp(mean(Percent_change3))-1)*100 ,
geoSE =( exp(mean(Percent_change3))*100*(sd(Percent_change3)/sqrt(n()))),n=n() )
percent_change_sum2$VISID <- "Percent_Change"
final_diff<-final_diff_10
if(split==30){final_diff<-final_diff_30}
if(split==300){final_diff<-final_diff_300}
if(split==3000){final_diff<-final_diff_max}
names(final_diff)<-c("Split","TRT","VISID","geomean","geoSE","Lower","Upper")
check <- merge(visit_sum, change_sum, all=TRUE)
check <- merge(check, percent_change_sum2, all=TRUE)
check <- merge(check, final_diff, all=TRUE)
check <- check[,c(1,2,3,6,5,4,7,8,9,10)]
check <- check[c(13,15,14,16,2,5,3,6,4,1,8,11,9,12,10,7),]
output<<-check
}
split_summary(ten,0)
write.csv(output, "Placebo_combined_<10_summary.csv")
split_summary(thirty,30)
write.csv(output, "Placebo_combined_<30_>10_summary.csv")
split_summary(threehundred,300)
write.csv(output, "Placebo_combined_<300_>30_summary.csv")
split_summary(overhundred,3000)
write.csv(output, "Placebo_combined_>300_summary.csv")
nicksun1/Biomarker-Analysis documentation built on Dec. 22, 2021, 2:14 a.m.
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Defines functions split_summary
# Biomarker Regression Analysis
#
# Log transformed analysis of combined biomarker data from studies A, CF, I, G
# Separates combined data in 4 different summary sets based on baseline biomarker value.
# Outputs csv file that includes all data split by thresholds where results of interest include %Change vs Placebo and Change in log(biomarker) vs Placebo
# Also outputs separate analysis and summary files for each individual threshold in format similar to individual study analysis output (studycf).
#
# Fit to model Change in log(biomarker) = Treatment and Fit to model log(y) = log(y_b) + Treatment
##
library(dplyr)
library(zoo)
library(EnvStats)
library(reshape)
suppressMessages(library(coastr))
## studycf
lab <- read.csv("labscf.csv")
studycf <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studycf <- studycf %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studycf %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter( VISID =="9") ###################
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[complete.cases(trt_merge[,14]),]
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studycf_comb <- trt_merge
studycf_comb$SUBJID <- paste("studycf",studycf_comb$SUBJID, sep="_")
## studya
lab <- read.csv("labsa.csv")
studya <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studya <- studya %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studya %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter(VISID =="10")
#trt_merge <- merge(studya, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studya_comb <- trt_merge
studya_comb$SUBJID <- paste("studya",studya_comb$SUBJID, sep="_")
## studyg
lab <- read.csv("labsg.csv")
adsl <- read.csv("adslg.csv")
studyg <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyg <- studyg %>% filter(PARAMCD =="ALBCS49C")
studyg <- studyg %>% filter(SAFFL=="Y")
studyg <- studyg %>% filter(AVISITN =="9")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyg, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyg_comb <- trt_merge
## studyi
lab <- read.csv("labsi.csv")
adsl <- read.csv("adsli.csv")
studyi <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyi <- studyi %>% filter(PARAMCD =="ALBCS49C")
studyi <- studyi %>% filter(SAFFL=="Y")
studyi <- studyi %>% filter(AVISITN =="204")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyi, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyi_comb <- trt_merge
#### Combine studies and Filter by biomarker value
combined <- merge(studycf_comb, studya_comb, all=TRUE)
combined <- merge(combined, studyi_comb, all=TRUE)
combined <- merge(combined, studyg_comb, all=TRUE)
ten <- combined %>% filter (base_unchanged<10)
thirty <- combined %>% filter (base_unchanged>=10 & base_unchanged<30)
threehundred <- combined %>% filter (base_unchanged>=30 & base_unchanged<300)
overhundred <- combined %>% filter (base_unchanged>=300)
ten$Split <- "<10"
thirty$Split<- ">=10 and <30"
threehundred$Split<- ">=30 and <300"
overhundred$Split<- ">=300"
num_patients <- merge(ten,thirty,all=TRUE)
num_patients <- merge(num_patients,threehundred,all=TRUE)
num_patients <- merge(num_patients,overhundred,all=TRUE)
num <- num_patients %>% group_by(TRT,Split) %>% summarise( n=n() )
combined_above <- overhundred
hold_percentchange <- combined_above %>% filter(VISID=="9" | VISID=="10" | VISID=="204")
hold_percentchange$Change[hold_percentchange$Change==0]<- NA
hold_percentchange <- hold_percentchange[complete.cases(hold_percentchange),]
percent_change_sum<- hold_percentchange %>% group_by(TRT) %>% summarise( geomean= (exp(mean(Percent_change3))-1)*100 ,
geoSE =( exp(mean(Percent_change3))*100*(sd(Percent_change3)/sqrt(n()))),n=n() )
percent_change_sum$delta <- percent_change_sum$geomean-percent_change_sum$geomean[percent_change_sum$TRT=="Placebo"]
percent_change_sum$deltaSE <- sqrt((percent_change_sum$geoSE[percent_change_sum$TRT=="Placebo"])^2+(percent_change_sum$geoSE)^2)
percent_change_sum$VISID <- "%Change vs Placebo"
percent_change_sum<-percent_change_sum[1:2,c(1,7,5,6)]
names(percent_change_sum)<-c("TRT","VISID","geomean","geoSE")
percent_change_sum$Lower <- percent_change_sum$geomean-qnorm(0.975)*percent_change_sum$geoSE
percent_change_sum$Upper <- percent_change_sum$geomean+qnorm(0.975)*percent_change_sum$geoSE
combined_above$TRT<-as.factor(combined_above$TRT)
combined_above$TRT = relevel(combined_above$TRT, ref="Placebo")
lm_data <- combined_above
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:3,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:3,1:2])
ci1$TRT <- "Drug_0.75"
ci1[2,3] <-"Drug_1.5"
ci1$VISID <-"Change in log(biomarker) vs Placebo"
names(ci1) <-c("Lower", "Upper","TRT","VISID")
trt_diff_merge <-merge(trt_diff,ci1,all=TRUE)
final_diff <- merge(percent_change_sum, trt_diff_merge,all=TRUE)
final_diff$Threshold <- "<10"
final_diff<- final_diff[,c(7,1,2,3,4,5,6)]
names(final_diff) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
final_diff_max <- final_diff
final_diff_30$Threshold <- ">=10 and <30"
final_diff_300$Threshold <- ">=30 and <300"
final_diff_max$Threshold <- ">=300"
final_merge<-merge(final_diff_10,final_diff_30,all=TRUE)
final_merge<-merge(final_merge,final_diff_300,all=TRUE)
final_merge<-merge(final_merge,final_diff_max,all=TRUE)
write.csv(final_merge, "Placebo_comparison_4_thresholds.csv")
#### Summary function
split_summary <-function( data, split){
data$VISID<-"Last Visit"
visit_sum_last <- data %>% group_by(TRT,VISID,Split) %>% summarise( mean= mean(AVAL),n=n(), sd =sd(AVAL), geomean =geoMean(aval_unchanged), geoSE= geoSD(aval_unchanged))
visit_sum_base <- data %>% group_by(TRT,VISID,Split) %>% summarise( mean= mean(BASE),n=n(), sd =sd(BASE), geomean =geoMean(base_unchanged), geoSE= geoSD(base_unchanged))
visit_sum_base$VISID <- "Baseline"
visit_sum <- merge(visit_sum_last, visit_sum_base,all=TRUE)
temp3 <- data
temp3$Change[temp3$Change==0]<- NA
temp3 <- temp3[complete.cases(temp3),]
hold2 <- temp3 %>% group_by(TRT, VISID,Split) %>% summarise( mean= mean(log(aval_unchanged)-log(base_unchanged)), n= n(), sd =(sd(log(aval_unchanged)-log(base_unchanged))),
geoSE =(sd(log(aval_unchanged)-log(base_unchanged))/sqrt(n())))
change_sum <- hold2
change_sum$VISID ="Change"
percent_change_sum2<- temp3 %>% group_by(TRT, VISID, Split) %>% summarise( geomean= (exp(mean(Percent_change3))-1)*100 ,
geoSE =( exp(mean(Percent_change3))*100*(sd(Percent_change3)/sqrt(n()))),n=n() )
percent_change_sum2$VISID <- "Percent_Change"
final_diff<-final_diff_10
if(split==30){final_diff<-final_diff_30}
if(split==300){final_diff<-final_diff_300}
if(split==3000){final_diff<-final_diff_max}
names(final_diff)<-c("Split","TRT","VISID","geomean","geoSE","Lower","Upper")
check <- merge(visit_sum, change_sum, all=TRUE)
check <- merge(check, percent_change_sum2, all=TRUE)
check <- merge(check, final_diff, all=TRUE)
check <- check[,c(1,2,3,6,5,4,7,8,9,10)]
check <- check[c(13,15,14,16,2,5,3,6,4,1,8,11,9,12,10,7),]
output<<-check
}
split_summary(ten,0)
write.csv(output, "Placebo_combined_<10_summary.csv")
split_summary(thirty,30)
write.csv(output, "Placebo_combined_<30_>10_summary.csv")
split_summary(threehundred,300)
write.csv(output, "Placebo_combined_<300_>30_summary.csv")
split_summary(overhundred,3000)
write.csv(output, "Placebo_combined_>300_summary.csv")
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