The R Journal: article published in 2017, volume 9:2

rpsftm: An R Package for Rank Preserving Structural Failure Time Models
Annabel Allison, Ian R White and Simon Bond , The R Journal (2017) 9:2, pages 342-353.

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, rpsftm, that implements the method.

Received: 2017-04-27; online 2017-12-04, supplementary material, (678 B)
CRAN packages: ipw, rpsftm, eha
CRAN Task Views implied by cited CRAN packages: Survival

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-068,
  author = {Annabel Allison and Ian R White and Simon Bond},
  title = {{rpsftm: An R Package for Rank Preserving Structural Failure
          Time Models}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-068},
  url = {https://doi.org/10.32614/RJ-2017-068},
  pages = {342--353},
  volume = {9},
  number = {2}
}