What safety signals emerge?

Current-view SVG rendering: TypeScript figure source and display controller. These render existing results; they do not run the R/Python analysis.

Full seven-case analysis pipeline. The exact executed file; use the commands below. This code is read-only.

# Clinical Data Lab: independently executed R analysis of CDISC Pilot XPT.
# Run: Rscript --vanilla analyze.R INPUT_DIRECTORY OUTPUT_DIRECTORY
# Packages must already be installed. No network or installation at runtime.
library(haven)
library(jsonlite)
library(survival)
args <- commandArgs(trailingOnly=TRUE)
input <- args[1]; output <- args[2]
dir.create(output, recursive=TRUE, showWarnings=FALSE)
read <- function(name) {
  x <- as.data.frame(read_xpt(file.path(input, paste0(name, '.xpt'))))
  x[] <- lapply(x, function(v) {if(is.character(v)) {v[is.na(v)] <- ''; trimws(v)} else v})
  x
}
stats <- function(x) {
  x <- x[!is.na(x)]; n <- length(x)
  m <- if(n) mean(x) else NA_real_
  s <- if(n>1) sd(x) else NA_real_
  margin <- if(n>1) qt(.975,n-1)*s/sqrt(n) else NA_real_
  list(n=n,estimate=m,sd=s,lower=m-margin,upper=m+margin)
}
adsl <- read('adsl'); ae <- read('adae'); q <- read('adqsadas'); lab <- read('adlbc'); tte <- read('adtte')
stopifnot(!anyDuplicated(adsl$USUBJID), !anyDuplicated(tte$USUBJID), all(tte$CNSR %in% c(0,1)))
stopifnot(all((adsl$DISCONFL=='Y')==(adsl$DCDECOD!='COMPLETED')))
q <- subset(q, PARAMCD=='ACTOT' & ANL01FL=='Y' & DTYPE=='')
stopifnot(!anyDuplicated(q[c('USUBJID','AVISIT')]))
lab <- subset(lab, PARAMCD=='ALT' & AVISIT=='End of Treatment')
stopifnot(!anyDuplicated(lab$USUBJID))
cases <- c('baseline','disposition','longitudinal','adverse-events','laboratory','subgroups','time-to-event')
out <- setNames(lapply(cases,function(x) list()),cases)
arms <- c('Placebo','Xanomeline Low Dose','Xanomeline High Dose')
terms <- unique(ae[c('AEBODSYS','AEDECOD')]); terms <- terms[order(terms$AEBODSYS,terms$AEDECOD),]
for(stratum in c('All','F','M')) {
  cohort <- if(stratum=='All') adsl else subset(adsl,SEX==stratum)
  for(arm in arms) {
    subjects <- subset(cohort,TRT01A==arm & SAFFL=='Y'); ids <- subjects$USUBJID; N <- length(ids)
    add <- function(case,measure,...) {
      out[[case]][[length(out[[case]])+1]] <<- c(list(stratum=stratum,arm=arm,measure=measure),list(...))
    }
    for(col in c('AGE','WEIGHTBL','BMIBL')) {
      label <- c(AGE='Age (years)',WEIGHTBL='Weight (kg)',BMIBL='BMI (kg/m^2)')[[col]]
      s <- stats(subjects[[col]])
      do.call(add,c(list(case='baseline',measure=label,denominator=N,missing=N-s$n),s))
    }
    for(col in c('SEX','RACE')) for(level in sort(unique(adsl[[col]]))) {
      n <- sum(subjects[[col]]==level)
      add('baseline',paste0(col,': ',level),n=n,denominator=N,missing=sum(subjects[[col]]==''),estimate=100*n/N)
    }
    add('disposition','Analysis cohort',n=N,denominator=N,estimate=100)
    for(reason in sort(unique(adsl$DCDECOD))) {
      n <- sum(subjects$DCDECOD==reason); add('disposition',reason,n=n,denominator=N,estimate=100*n/N)
    }
    qi <- subset(q,USUBJID %in% ids)
    for(visit in c('Baseline','Week 8','Week 16','Week 24')) for(col in c('AVAL','CHG')) {
      s <- stats(qi[qi$AVISIT==visit,col]); label <- if(col=='AVAL') 'ADAS-Cog(11) score' else 'Change from baseline'
      time <- c(Baseline=0,'Week 8'=8,'Week 16'=16,'Week 24'=24)[[visit]]
      do.call(add,c(list(case='longitudinal',measure=label,visit=visit,time=time,denominator=N,missing=N-s$n),s))
    }
    ai <- subset(ae,USUBJID %in% ids & TRTEMFL=='Y')
    ae_row <- function(d,label,soc,level) add('adverse-events',label,soc=soc,level=level,n=length(unique(d$USUBJID)),events=nrow(d),denominator=N,estimate=100*length(unique(d$USUBJID))/N)
    ae_row(ai,'Any treatment-emergent AE','','Any')
    for(soc in sort(unique(ae$AEBODSYS))) ae_row(ai[ai$AEBODSYS==soc,],soc,soc,'SOC')
    for(i in seq_len(nrow(terms))) {
      soc <- terms$AEBODSYS[i]; pt <- terms$AEDECOD[i]
      ae_row(ai[ai$AEBODSYS==soc & ai$AEDECOD==pt,],pt,soc,'PT')
    }
    li <- subset(lab,USUBJID %in% ids & BNRIND %in% c('L','N','H') & ANRIND %in% c('L','N','H'))
    for(base in c('L','N','H')) for(follow in c('L','N','H')) {
      n <- sum(li$BNRIND==base & li$ANRIND==follow)
      add('laboratory',paste0(base,' -> ',follow),n=n,denominator=nrow(li),missing=N-nrow(li),estimate=if(nrow(li)) 100*n/nrow(li) else NA_real_)
    }
    ti <- subset(tte,USUBJID %in% ids & PARAMCD=='TTDE')
    stopifnot(nrow(ti)==N,all(!is.na(ti$AVAL)))
    fit <- survfit(Surv(AVAL,1-CNSR)~1,data=ti,conf.type='log-log')
    add('time-to-event','Event-free probability',time=0,risk=N,events=0,censored=0,estimate=1,lower=1,upper=1,denominator=N)
    for(i in seq_along(fit$time)) add('time-to-event','Event-free probability',time=fit$time[i],risk=fit$n.risk[i],events=fit$n.event[i],censored=fit$n.censor[i],estimate=fit$surv[i],lower=fit$lower[i],upper=fit$upper[i],denominator=N)
  }
  for(label in c('Overall','Age <65','Age >=65')) {
    group <- subset(cohort,ITTFL=='Y')
    if(label=='Age <65') group <- subset(group,AGE<65)
    if(label=='Age >=65') group <- subset(group,AGE>=65)
    end <- subset(q,AVISIT=='Week 24' & USUBJID %in% group$USUBJID)
    placebo <- end$CHG[end$USUBJID %in% group$USUBJID[group$TRT01P=='Placebo']]; placebo <- na.omit(placebo)
    for(arm in arms[-1]) {
      active <- end$CHG[end$USUBJID %in% group$USUBJID[group$TRT01P==arm]]; active <- na.omit(active)
      est <- lo <- hi <- NA_real_
      if(min(length(active),length(placebo))>=2) {
        test <- t.test(active,placebo,var.equal=FALSE,conf.level=.95)
        est <- mean(active)-mean(placebo); lo <- test$conf.int[1]; hi <- test$conf.int[2]
      }
      N <- sum(group$TRT01P==arm)
      out[['subgroups']][[length(out[['subgroups']])+1]] <- list(stratum=stratum,arm=arm,measure=label,n=length(active),control_n=length(placebo),denominator=N,missing=N-length(active),estimate=est,lower=lo,upper=hi)
    }
  }
}
for(case in cases) write_json(out[[case]],file.path(output,paste0(case,'.json')),auto_unbox=TRUE,pretty=TRUE,digits=16,na='null')
f <- stats(c(1,2,3,NA)); stopifnot(f$n==3,f$estimate==2,f$sd==1)
k <- survfit(Surv(c(1,2,2,3),c(1,1,0,1))~1)
stopifnot(abs(k$surv[2]-.5)<1e-12,k$n.risk[2]==3)
packages <- c('haven','jsonlite','survival','ggplot2')
write_json(list(R=as.character(getRversion()),packages=setNames(lapply(packages,function(p) as.character(packageVersion(p))),packages),fixtures='pass',seed='not applicable: deterministic'),file.path(output,'environment.json'),auto_unbox=TRUE,pretty=TRUE)
print(sapply(out,length)); print(sessionInfo())