title: Inference in randomized trials with death and missingness.
abstract: 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.
authors.affiliation: Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, U.S.A.
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.
authors.forename: Daniel O
authors.initials: DO
authors.lastname: Scharfstein
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Colantuoni
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Girard
authors.affiliation: Helsinn Therapeutics (U.S.), Inc., Iselin, New Jersey, U.S.A.
authors.forename: Ying
authors.initials: Y
authors.lastname: Yan
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung; D005260:Female; D006801:Humans; D008175:Lung Neoplasms; D011247:Pregnancy; D047928:Premature Birth; D016032:Randomized Controlled Trials as Topic
keywords: Composite endpoint; Death-truncated data; Missing data; Sensitivity analysis