Journal
BIOMETRIKA
Volume 99, Issue 1, Pages 167-184Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asr062
Keywords
Expectation-maximization algorithm; Maximum likelihood estimate; Noncompliance; Panitumumab; Partial switching; Transition model; Treatment switching
Funding
- National Institutes of Health, U.S.A.
- Amgen Inc.
Ask authors/readers for more resources
Treatment switching is a frequent occurrence in clinical trials, where, during the course of the trial, patients who fail on the control treatment may change to the experimental treatment. Analysing the data without accounting for switching yields highly biased and inefficient estimates of the treatment effect. In this paper, we propose a novel class of semiparametric semicompeting risks transition survival models to accommodate treatment switches. Theoretical properties of the proposed model are examined and an efficient expectation-maximization algorithm is derived for obtaining the maximum likelihood estimates. Simulation studies are conducted to demonstrate the superiority of the model compared with the intent-to-treat analysis and other methods proposed in the literature. The proposed method is applied to data from a colorectal cancer clinical trial.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available