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Generate-Summary-Statistics

Vignette Build Datetime

message(paste0('Datetime: ',Sys.Date(),':',Sys.time()))
#> Datetime: 2026-01-21:2026-01-21 09:14:31

Load Libraries

library(repfun)
library(dplyr)

Set Up the Reporting Environment

tmpdr <- tempdir()
datdir <- file.path(gsub("\\","/",tmpdr,fixed=TRUE),"datdir")
dir.create(datdir,showWarnings=FALSE)
repfun::copydata(datdir)
repfun::rs_setup(D_POPDATA=repfun::adsl %>% 
           dplyr::filter(SAFFL =='Y'),
         D_SUBJID=c("STUDYID","USUBJID"),
         R_ADAMDATA=datdir)

Update ADSL and ADVS

repfun:::rfenv$G_POPDATA %>% dplyr::mutate(TRT01AN=ifelse(TRT01A=='Placebo',1,ifelse(TRT01A=='Xanomeline Low Dose',2,3))) -> G_POPDATA
attr(G_POPDATA$TRT01AN,"label") <- 'Actual Treatment for Period 01 (n)'

repfun:::rfenv$adamdata$advs.rda() %>% dplyr::filter(ANL01FL=='Y') %>% 
  dplyr::mutate(TRT01AN=ifelse(TRT01A=='Placebo',1,ifelse(TRT01A=='Xanomeline Low Dose',2,3))) %>%
  dplyr::filter(!is.na(AVISITN) & (DTYPE=='AVERAGE')) -> advs2
attr(advs2$TRT01AN,"label") <- 'Actual Treatment for Period 01 (n)'

Generate Summary Statistics for Baseline Characteristics

repfun::ru_sumstats(G_POPDATA,
            analysisvars=c("AGE","TRTDURD"),
            groupbyvars=c("STUDYID","TRT01AN"),
            codedecodevarpairs=c("TRT01AN", "TRT01A"),
            totalforvar="TRT01AN", totalid=99,
            totaldecode="Total",
            statsinrowsyn = "Y",
            analysisvardps=list("AGE"=1,"TRTDURD"=2),
            statslist=c("n", "mean", "median", "sd", "min", "max")) %>% 
   dplyr::arrange(tt_avid, TRT01AN,tt_svid) %>% dplyr::select(-tt_result_num) -> basechars

Display the Results for Baseline Characteristics

lbls <- sapply(basechars,function(x){attr(x,"label")})
knitr::kable(head(basechars,10), col.names=paste(names(lbls),lbls,sep=" "), 
             caption = "Summary Statistics for Baseline Characteristics")
Summary Statistics for Baseline Characteristics
STUDYID Study Identifier TRT01AN Actual Treatment for Period 01 (n) TRT01A Actual Treatment for Period 01 tt_result Result tt_svid Statistical Parameter ID tt_svnm Statistical Parameter Name tt_avid Analysis Variable ID tt_avnm Analysis Variable Name
CDISCPILOT01 1 Placebo 86 1 n 1 AGE
CDISCPILOT01 1 Placebo 75.21 2 Mean 1 AGE
CDISCPILOT01 1 Placebo 76.00 3 Median 1 AGE
CDISCPILOT01 1 Placebo 8.590 4 SD 1 AGE
CDISCPILOT01 1 Placebo 52.0 5 Min 1 AGE
CDISCPILOT01 1 Placebo 89.0 6 Max 1 AGE
CDISCPILOT01 2 Xanomeline Low Dose 96 1 n 1 AGE
CDISCPILOT01 2 Xanomeline Low Dose 75.96 2 Mean 1 AGE
CDISCPILOT01 2 Xanomeline Low Dose 78.00 3 Median 1 AGE
CDISCPILOT01 2 Xanomeline Low Dose 8.114 4 SD 1 AGE

Generate Summary Statistics for Vital Signs with Constant Precision

repfun::ru_sumstats(advs2,
            analysisvars=c("AVAL"),
            groupbyvars=c("STUDYID","TRT01AN","PARAMCD","AVISITN"),
            codedecodevarpairs=c("TRT01AN","TRT01A","PARAMCD","PARAM","AVISITN","AVISIT"),
            totalforvar="TRT01AN",
            totalid=99,
            totaldecode="Total",
            statsinrowsyn = "Y",
            analysisvardps=1,
            statslist=c("n","mean","median","sd","min","max")) %>% 
            dplyr::arrange(TRT01AN,PARAMCD,AVISITN, tt_svid) %>%     
            dplyr::select(-c('tt_avnm','tt_avid','tt_result_num')) -> vtlsigns

Display the Results for Vital Signs with Constant Precision

lbls <- sapply(vtlsigns,function(x){attr(x,"label")})
knitr::kable(head(vtlsigns,10), col.names=paste(names(lbls),lbls,sep=" "), 
             caption = "Summary Statistics for Vital Signs with Constant Precision")
Summary Statistics for Vital Signs with Constant Precision
STUDYID Study Identifier TRT01AN Actual Treatment for Period 01 (n) TRT01A Actual Treatment for Period 01 PARAMCD Parameter Code PARAM Parameter AVISITN Analysis Visit (N) AVISIT Analysis Visit tt_result Result tt_svid Statistical Parameter ID tt_svnm Statistical Parameter Name
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 63 1 n
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 23.29 2 Mean
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 23.04 3 Median
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 3.595 4 SD
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 15.3 5 Min
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 2 Week 2 33.6 6 Max
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 4 Week 4 59 1 n
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 4 Week 4 23.17 2 Mean
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 4 Week 4 23.01 3 Median
CDISCPILOT01 1 Placebo BMI Body Mass Index(kg/m^2) 4 Week 4 3.401 4 SD

Generate Summary Statistics for Vital Signs with Varying Precision

decodes <- advs2 %>% distinct(PARAMCD, PARAM)
dcodelst <- split(decodes$PARAM, decodes$PARAMCD)
advs2 %>% dplyr::select(STUDYID,USUBJID,TRT01AN,TRT01A,PARAMCD,AVISITN,AVISIT,AVAL) %>% 
  dplyr::arrange(USUBJID,TRT01AN,TRT01A,AVISITN,AVISIT) %>%
  dplyr::group_by(USUBJID,TRT01AN,TRT01A,AVISITN,AVISIT) %>% 
  tidyr::pivot_wider(names_from=PARAMCD, values_from=AVAL) -> advs2_t
advs2_t <- repfun::ru_labels(advs2_t,varlabels=dcodelst)
repfun::ru_sumstats(advs2_t,
            analysisvars=c("BMI","BSA","DIABP","MAP","PULSE","SYSBP","TEMP","WEIGHT"),
            groupbyvars=c("STUDYID","TRT01AN","AVISITN"),
            codedecodevarpairs=c("TRT01AN","TRT01A","AVISITN","AVISIT"),
            totalforvar="TRT01AN", totalid=99,
            totaldecode="Total",
            statsinrowsyn = "Y",
            analysisvardps=list("BMI"=1,"BSA"=2,"DIABP"=3,"MAP"=4,"PULSE"=1,"SYSBP"=2,
                                "TEMP"=3,"WEIGHT"=4),
            statslist=c("n","mean","median","sd","min","max")) %>% 
  dplyr::left_join(decodes %>% dplyr::mutate(tt_avnm=PARAMCD),by='tt_avnm') %>% 
  dplyr::arrange(tt_avid, TRT01AN, AVISITN, tt_svid) %>%
  dplyr::select(-c('tt_result_num','tt_avnm')) %>% 
  dplyr::arrange(PARAMCD,TRT01AN,AVISITN) -> vtlsigns_t

Display the Results for Vital Signs with Constant Precision

lbls <- sapply(vtlsigns_t,function(x){attr(x,"label")})
knitr::kable(head(vtlsigns_t,10), col.names=paste(names(lbls),lbls,sep=" "), 
             caption = "Summary Statistics for Vital Signs with Varying Precision")
Summary Statistics for Vital Signs with Varying Precision
STUDYID Study Identifier TRT01AN Actual Treatment for Period 01 (n) TRT01A Actual Treatment for Period 01 AVISITN Analysis Visit (N) AVISIT Analysis Visit tt_result Result tt_svid Statistical Parameter ID tt_svnm Statistical Parameter Name tt_avid Analysis Variable ID PARAMCD Parameter Code PARAM Parameter
CDISCPILOT01 1 Placebo 2 Week 2 63 1 n 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 2 Week 2 23.29 2 Mean 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 2 Week 2 23.04 3 Median 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 2 Week 2 3.595 4 SD 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 2 Week 2 15.3 5 Min 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 2 Week 2 33.6 6 Max 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 4 Week 4 59 1 n 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 4 Week 4 23.17 2 Mean 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 4 Week 4 23.01 3 Median 1 BMI Body Mass Index(kg/m^2)
CDISCPILOT01 1 Placebo 4 Week 4 3.401 4 SD 1 BMI Body Mass Index(kg/m^2)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.