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Inference in randomized trials with death and missingness
Author(s) -
Wang Chenguang,
Scharfstein Daniel O.,
Colantuoni Elizabeth,
Girard Timothy D.,
Yan Ying
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12594
Subject(s) - missing data , inference , randomized controlled trial , statistics , econometrics , computer science , mathematics , medicine , artificial intelligence
Summary 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 article, we propose a procedure for comparing treatments that is based on a composite endpoint that combines information on both the functional outcome and survival. We further propose a missing data imputation scheme and sensitivity analysis strategy to handle the unobserved functional outcomes not due to death. Illustrations of the proposed method are given by analyzing data from a recent non‐small cell lung cancer clinical trial and a recent trial of sedation interruption among mechanically ventilated patients.