Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.pdf (1.53 MB)
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Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.

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journal contribution
posted on 24.06.2019, 08:48 by N. R. Latimer, K. R. Abrams, U. Siebert
BACKGROUND: Treatment switching is common in randomised trials of oncology treatments, with control group patients switching onto the experimental treatment during follow-up. This distorts an intention-to-treat comparison of the treatments under investigation. Two-stage estimation (TSE) can be used to estimate counterfactual survival times for patients who switch treatments - that is, survival times that would have been observed in the absence of switching. However, when switchers do not die during the study, counterfactual censoring times are estimated, inducing informative censoring. Re-censoring is usually applied alongside TSE to resolve this problem, but results in lost longer-term information - a major concern if the objective is to estimate long-term treatment effects, as is usually the case in health technology assessment. Inverse probability of censoring weights (IPCW) represents an alternative technique for addressing informative censoring but has not before been combined with TSE. We aim to determine whether combining TSE with IPCW (TSEipcw) represents a valid alternative to re-censoring. METHODS: We conducted a simulation study to compare TSEipcw to TSE with and without re-censoring. We simulated 48 scenarios where control group patients could switch onto the experimental treatment, with switching affected by prognosis. We investigated various switching proportions, treatment effects, survival function shapes, disease severities and switcher prognoses. We assessed the alternative TSE applications according to their estimation of control group restricted mean survival (RMST) that would have been observed in the absence of switching up to the end of trial follow-up. RESULTS: TSEipcw performed well when its weights had a low coefficient of variation, but performed poorly when the coefficient of variation was high. Re-censored analyses usually under-estimated control group RMST, whereas non-re-censored analyses usually produced over-estimates, with bias more serious when the treatment effect was high. In scenarios where TSEipcw performed well, it produced low bias that was often between the two extremes associated with the re-censoring and non-recensoring options. CONCLUSIONS: Treatment switching adjustment analyses using TSE should be conducted with re-censoring, without re-censoring, and with IPCW to explore the sensitivity in results to these application options. This should allow analysts and decision-makers to better interpret the results of adjustment analyses.

Funding

NRL was supported by the National Institute for Health Research (NIHR Post Doctoral Fellowship, Dr. Nicholas Latimer, PDF-2015-08-022). NRL is now funded by Yorkshire Cancer Research (Award reference number S406NL). KRA was partially supported by the National Institute for Health Research (NIHR) as a Senior Investigator [NF-SI-0512-10159] & is a NIHR Senior Investigator Emeritus. US was in part supported by the COMET Center ONCOTYROL (Grant no. 2073085), which is funded by the Austrian Federal Ministries BMVIT/BMWFJ (via FFG) and the Tiroler Zukunftsstiftung/Standortagentur Tirol (SAT).

History

Citation

BMC Medical Research Methodology, 2019, 19:69

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

VoR (Version of Record)

Published in

BMC Medical Research Methodology

Publisher

BMC (part of Springer Nature)

eissn

1471-2288

Acceptance date

14/03/2019

Copyright date

2019

Available date

24/06/2019

Publisher version

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0709-9

Language

en