Adjusting overall survival for treatment switches: commonly used methods and practical application. (18th October 2013)

• Record Type: Journal Article
• Authors: Watkins, Claire; Huang, Xin; Latimer, Nicholas; Tang, Yiyun; Wright, Elaine J.
• Is Part Of: Pharmaceutical statistics. Volume 12:Number 6(2013:Nov./Dec.)
• Page Start: 348
• Page End: 357
• Publication Date: 2013-10-18
• Subjects: treatment switching -- crossover -- inverse probability of censoring weighting -- rank‐preserving structural failure time -- health technology assessment; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1602
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : In parallel group trials, long‐term efficacy endpoints may be affected if some patients switch or cross over to the alternative treatment arm prior to the event. In oncology trials, switch to the experimental treatment can occur in the control arm following disease progression and potentially impact overall survival. It may be a clinically relevant question to estimate the efficacy that would have been observed if no patients had switched, for example, to estimate 'real‐life' clinical effectiveness for a health technology assessment. Several commonly used statistical methods are available that try to adjust time‐to‐event data to account for treatment switching, ranging from naive exclusion and censoring approaches to more complex inverse probability of censoring weighting and rank‐preserving structural failure time models. These are described, along with their key assumptions, strengths, and limitations. Best practice guidance is provided for both trial design and analysis when switching is anticipated. Available statistical software is summarized, and examples are provided of the application of these methods in health technology assessments of oncology trials. Key considerations include having a clearly articulated rationale and research question and a well‐designed trial with sufficient good quality data collection to enable robust statistical analysis. No analysis method is universally suitable in all situations, and each makes strong untestable assumptions. There is a need for further research into new or improved techniques. This information should aid statisticians and their colleagues to improve the design and analysis of clinical trials where treatment switch is anticipated. Copyright © 2013 John Wiley & Sons, Ltd.