A Simple Method for Principal Strata Effects When the Outcome Has Been Truncated Due to Death
Open Access
- 25 February 2011
- journal article
- research article
- Published by Oxford University Press (OUP) in American Journal of Epidemiology
- Vol. 173 (7), 745-751
- https://doi.org/10.1093/aje/kwq418
Abstract
In randomized trials with follow-up, outcomes such as quality of life may be undefined for individuals who die before the follow-up is complete. In such settings, restricting analysis to those who survive can give rise to biased outcome comparisons. An alternative approach is to consider the “principal strata effect” or “survivor average causal effect” (SACE), defined as the effect of treatment on the outcome among the subpopulation that would have survived under either treatment arm. The authors describe a very simple technique that can be used to assess the SACE. They give both a sensitivity analysis technique and conditions under which a crude comparison provides a conservative estimate of the SACE. The method is illustrated using data from the ARDSnet (Acute Respiratory Distress Syndrome Network) clinical trial comparing low-volume ventilation and traditional ventilation methods for individuals with acute respiratory distress syndrome.Keywords
This publication has 25 references indexed in Scilit:
- Sharp bounds on the causal effects in randomized experiments with “truncation-by-death”Statistics & Probability Letters, 2007
- Application of the Principal Stratification Approach to the Faenza Randomized Experiment on Breast Self‐ExaminationBiometrics, 2006
- Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to DeathStatistical Science, 2006
- Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deathsBiostatistics, 2005
- An Estimator for Treatment Comparisons among Survivors in Randomized TrialsBiometrics, 2005
- Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”Journal of Educational and Behavioral Statistics, 2003
- Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine TrialsBiometrics, 2003
- On the analysis of viral load endpoints in HIV vaccine trialsStatistics in Medicine, 2003
- Principal Stratification in Causal InferenceBiometrics, 2002
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effectMathematical Modelling, 1986