Stat Methods Med Res-2016-Latimer-0962280216642264.pdf (260.95 kB)
Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study
journal contributionposted on 2016-05-16, 12:43 authored by Nicholas R. Latimer, Keith R. Abrams, Paul C. Lambert, James P. Morden, Michael J. Crowther
When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.
Keith Abrams is partially supported as a Senior Investigator by the National Institute for Health Research (NIHR) in the UK (NI-SI-0508-10061). Michael Crowther is partially supported by a National Institute for Health Research (NIHR) Doctoral Research Fellowship (DRF-2012-05-409). James Morden works for the ICR-CTSU, which receives core funding from Cancer Research UK. This work was supported by the Pharmaceutical Oncology Initiative, a group of pharmaceutical companies who are part of the Association of the British Pharmaceutical Industry (ABPI) and the National Institute for Health Research (NIHR) (grant numbers NI-SI-0508-10061, DRF-2012-05-409).
CitationStatistical Methods in Medical Research, 2016, in press
Author affiliation/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences
- VoR (Version of Record)