Inference in Randomized Controlled Trials with Death and Missingness
Inference in Randomized Clinical Trials with Death and Missingness
Create data for IDEM analysis
Imputation model fitting
Impute missing data
Impute missing data by mice
Impute missing data for MCMC convergence checking
Treatment effect estimation and hypothesis testing
Run Web-Based idem application
Plot of IDEMDATA object
Plot model fitting results
Plot imputation results
Plot hypothesis testing results
Plot MCMC mixing results
Plot survivors only and SACE analysis results
Print IDEMDATA object
Print error messages
Print model fitting results
Print imputation results
Print inference results
Print MCMC mixing checking result
Print survivors only or SACE analysis results
Summary of IDEMDATA object
Summary of the inference results
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) <DOI:10.1111/biom.12594> and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data imputation with different sensitivity analysis strategies to handle the unobserved functional outcomes not due to death.