Literature DB >> 33273895

idem: An R Package for Inferences in Clinical Trials with Death and Missingness.

Chenguang Wang1, Elizabeth Colantuoni2, Andrew Leroux2, Daniel O Scharfstein2.   

Abstract

In randomized controlled trials of seriously ill patients, death is common and often defined as the primary endpoint. Increasingly, non-mortality outcomes such as functional outcomes are co-primary or secondary endpoints. Functional outcomes are not defined for patients who die, referred to as "truncation due to death", and among survivors, functional outcomes are often unobserved due to missed clinic visits or loss to follow-up. It is well known that if the functional outcomes "truncated due to death" or missing are handled inappropriately, treatment effect estimation can be biased. In this paper, we describe the package idem that implements a procedure for comparing treatments that is based on a composite endpoint of mortality and the functional outcome among survivors. Among survivors, the procedure incorporates a missing data imputation procedure with a sensitivity analysis strategy. A web-based graphical user interface is provided in the idem package to facilitate users conducting the proposed analysis in an interactive and user-friendly manner. We demonstrate idem using data from a recent trial of sedation interruption among mechanically ventilated patients.

Entities:  

Keywords:  R; SACE; Stan; clinical trial; composite endpoint; imputation; missing data; sensitivity analysis; shiny; truncation due to death

Year:  2020        PMID: 33273895      PMCID: PMC7710152          DOI: 10.18637/jss.v093.i12

Source DB:  PubMed          Journal:  J Stat Softw        ISSN: 1548-7660            Impact factor:   6.440


  8 in total

Review 1.  Worst-rank score analysis with informatively missing observations in clinical trials.

Authors:  J M Lachin
Journal:  Control Clin Trials       Date:  1999-10

2.  Principal stratification in causal inference.

Authors:  Constantine E Frangakis; Donald B Rubin
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

3.  An estimator for treatment comparisons among survivors in randomized trials.

Authors:  Douglas Hayden; Donna K Pauler; David Schoenfeld
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

4.  Treatment comparisons for a partially categorical outcome applied to a biomarker with assay limit.

Authors:  Y H Joshua Chen; A Lawrence Gould; Michael L Nessly
Journal:  Stat Med       Date:  2005-01-30       Impact factor: 2.373

5.  A simple method for principal strata effects when the outcome has been truncated due to death.

Authors:  Yasutaka Chiba; Tyler J VanderWeele
Journal:  Am J Epidemiol       Date:  2011-02-25       Impact factor: 4.897

6.  Transforming self-rated health and the SF-36 scales to include death and improve interpretability.

Authors:  P Diehr; D L Patrick; J Spertus; C I Kiefe; M McDonell; S D Fihn
Journal:  Med Care       Date:  2001-07       Impact factor: 2.983

7.  Inference in randomized trials with death and missingness.

Authors:  Chenguang Wang; Daniel O Scharfstein; Elizabeth Colantuoni; Timothy D Girard; Ying Yan
Journal:  Biometrics       Date:  2016-10-17       Impact factor: 2.571

8.  Efficacy and safety of a paired sedation and ventilator weaning protocol for mechanically ventilated patients in intensive care (Awakening and Breathing Controlled trial): a randomised controlled trial.

Authors:  Timothy D Girard; John P Kress; Barry D Fuchs; Jason W W Thomason; William D Schweickert; Brenda T Pun; Darren B Taichman; Jan G Dunn; Anne S Pohlman; Paul A Kinniry; James C Jackson; Angelo E Canonico; Richard W Light; Ayumi K Shintani; Jennifer L Thompson; Sharon M Gordon; Jesse B Hall; Robert S Dittus; Gordon R Bernard; E Wesley Ely
Journal:  Lancet       Date:  2008-01-12       Impact factor: 79.321

  8 in total

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