Latimer, N.R. orcid.org/0000-0001-5304-5585, Abrams, K.R., Lambert, P.C. et al. (2 more authors) (2018) Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study. Statistical Methods in Medical Research, 27 (3). pp. 765-784. ISSN 0962-2802
Abstract
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 intentionto-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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Keywords: | Treatment switching; treatment crossover; survival analysis; overall survival; oncology; health technology assessment; time-to-event outcomes; prediction |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Funding Information: | Funder Grant number DEPARTMENT OF HEALTH DRF-2009-02-82 PHARMACEUTICAL ONCOLOGY INITIATIVE 74119 100CC |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jun 2017 15:03 |
Last Modified: | 27 Aug 2019 11:22 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/0962280216642264 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117136 |