Inference in Randomized Controlled Trials with Death and Missingness

In randomized studies involving severely ill patients, functional outcomes are often unobserved due to missed clinic visits, premature withdrawal or death. It is well known that if these unobserved functional outcomes are not handled properly, biased treatment comparisons can be produced. In this package, we implement a procedure for comparing treatments that is based on the composite endpoint of both the functional outcome and survival. The procedure was proposed in Wang et al. (2016) and Wang et al. (2020) . It considers missing data imputation with different sensitivity analysis strategies to handle the unobserved functional outcomes not due to death.


idem 3.0

  • Added a file to track changes to the package.
  • Reorganized functions with S3 methods implemented for most of the classes
  • Added SACE analysis

Reference manual

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5.1 by Chenguang Wang, 10 months ago

Browse source code at

Authors: Chenguang Wang [aut, cre] , Andrew Leroux [aut, cre] , Elizabeth Colantuoni [aut] , Daniel O Scharfstein [aut] , Trustees of Columbia University [cph] (tools/make_cpp.R , R/stanmodels.R)

Documentation:   PDF Manual  

Task views: Missing Data

GPL (>= 3) license

Imports rstan, sqldf, survival, mice, parallel

Depends on Rcpp, methods

Suggests knitr, shiny, rmarkdown, pander, DT, shinythemes

Linking to StanHeaders, rstan, BH, Rcpp, RcppEigen

System requirements: GNU make

See at CRAN