Volume 73, Issue 2
BIOMETRIC METHODOLOGY

Inference in randomized trials with death and missingness

Chenguang Wang

Corresponding Author

E-mail address: cwang68@jhmi.edu

Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, U.S.A.

email: cwang68@jhmi.edu

Search for more papers by this author
Daniel O. Scharfstein

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.

Search for more papers by this author
Elizabeth Colantuoni

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.

Search for more papers by this author
Timothy D. Girard

Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A.

Search for more papers by this author
Ying Yan

Helsinn Therapeutics (U.S.), Inc., Iselin, New Jersey, U.S.A.

Search for more papers by this author
First published: 17 October 2016
Citations: 8

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.

Number of times cited according to CrossRef: 8

  • Assessing the Course of Organ Dysfunction Using Joint Longitudinal and Time-to-Event Modeling in the Vasopressin and Septic Shock Trial, Critical Care Explorations, 10.1097/CCE.0000000000000104, 2, 4, (e0104), (2020).
  • Efficient Multiple Imputation for Sensitivity Analysis of Recurrent Events Data with Informative Censoring, Statistics in Biopharmaceutical Research, 10.1080/19466315.2020.1819403, (1), (2020).
  • Contemporary strategies to improve clinical trial design for critical care research: insights from the First Critical Care Clinical Trialists Workshop, Intensive Care Medicine, 10.1007/s00134-020-05934-6, (2020).
  • Measuring and Analyzing Length of Stay in Critical Care Trials, Medical Care, 10.1097/MLR.0000000000001059, 57, 9, (e53-e59), (2019).
  • Estimating Mann–Whitney‐type Causal Effects, International Statistical Review, 10.1111/insr.12326, 87, 3, (514-530), (2019).
  • Efficient Estimation of Mann–Whitney-Type Effect Measures for Right-Censored Survival Outcomes in Randomized Clinical Trials, Statistics in Biosciences, 10.1007/s12561-019-09246-2, (2019).
  • Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes, Journal of Causal Inference, 10.1515/jci-2018-0010, 0, 0, (2018).
  • Dealing with death when studying disease or physiological marker: the stochastic system approach to causality, Lifetime Data Analysis, 10.1007/s10985-018-9454-3, (2018).

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.