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idem (version 5.2)

Inference in Randomized Controlled Trials with Death and Missingness

Description

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.

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Install

install.packages('idem')

Monthly Downloads

314

Version

5.2

License

GPL (>= 3)

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Last Published

August 9th, 2023

Functions in idem (5.2)

print.IDEMSINGLE

Print MCMC mixing checking result
plot.IDEMSINGLE

Plot MCMC mixing results
print.IDEMIMP

Print imputation results
print.IDEMINFER

Print inference results
plot.IDEMIMP

Plot imputation results
plot.IDEMINFER

Plot hypothesis testing results
summary.IDEMDATA

Summary of IDEMDATA object
summary.IDEMINFER

Summary of the inference results
print.IDEMDATA

Print IDEMDATA object
print.summary.IDEMINFER

Print survivors only or SACE analysis results
print.IDEMFIT

Print model fitting results
imData

Create data for IDEM analysis
imImpSingle

Impute missing data for MCMC convergence checking
imShiny

Run Web-Based idem application
plot.IDEMDATA

Plot of IDEMDATA object
print.IDEMERROR

Print error messages
imFitModel

Imputation model fitting
abc

Example dataset
idem-package

Inference in Randomized Clinical Trials with Death and Missingness
plot.summary.IDEMINFER

Plot survivors only and SACE analysis results
imImpAll

Impute missing data
plot.IDEMFIT

Plot model fitting results
imImpAll_mice

Impute missing data by mice
imInfer

Treatment effect estimation and hypothesis testing