Abstract
Treatment switching in a randomised controlled trial occurs when participants change from their randomised treatment to the other trial treatment during the study. Failure to account for treatment switching in the analysis (i.e. by performing a standard intention-to-treat analysis) can lead to biased estimates of treatment efficacy. The rank preserving structural failure time model (RPSFTM) is a method used to adjust for treatment switching in trials with survival outcomes. The RPSFTM is due to Robins and Tsiatis (1991) and has been developed by White et al. (1997, 1999). The method is randomisation based and uses only the randomised treatment group, observed event times, and treatment history in order to estimate a causal treatment effect. The treatment effect, ψ, is estimated by balancing counter-factual event times (that would be observed if no treatment were received) between treatment groups. G-estimation is used to find the value of ψ such that a test statistic Z(ψ) = 0. This is usually the test statistic used in the intention-to-treat analysis, for example, the log rank test statistic. We present an R package that implements the method of rpsftm.
Highlights
In a two-arm randomised controlled trial, participants are randomly allocated to receive one of two treatments or interventions
This paper focuses on the case of treatment switching over time, where participants may switch to receive the other trial treatment during the trial (Latimer et al, 2014)
A common example is in a trial of active treatment versus placebo where placebo arm participants may start the active treatment in response to disease progression
Summary
In a two-arm randomised controlled trial, participants are randomly allocated to receive one of two treatments or interventions. The fitted model is rpsftm_fit_lr
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