ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials

In randomized clinical trials (RCT), the analysis is based on the intent-to-treat principle to avoid any selection bias in the constitution of groups. However, estimates of overall survival can be biased when significant crossover occurs because the separation of randomized groups is lost. To handle...

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Bibliographic Details
Published in:Computers in biology and medicine 2019-08, Vol.111, p.103339-103339, Article 103339
Main Authors: Grafféo, Nathalie, Latouche, Aurélien, Le Tourneau, Christophe, Chevret, Sylvie
Format: Article
Language:English
Subjects:
Applications
Bias
Clinical trials
Constitution
Inverse probability weighting
Methods
Patients
Randomization
Randomized clinical trial
Statistics
Survival
Survival analysis
Switches
Time dependence
Treatment switch
Tumors
Weighting
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Summary:In randomized clinical trials (RCT), the analysis is based on the intent-to-treat principle to avoid any selection bias in the constitution of groups. However, estimates of overall survival can be biased when significant crossover occurs because the separation of randomized groups is lost. To handle these switches, the inverse probability of censoring weighting (IPCW) method has been proposed; however, it is still poorly used in RCT, notably because of its complex implementation. In particular, for time-to-event outcomes, it can be difficult to format data, especially when time-dependent covariates have to be managed, with different measurement times between patients. This paper aims to present the R package ipcwswitch with some guidance for the analysis of the treatment effect on survival in a hypothetical setting where all patients would have continued to take the randomization treatment. After a brief recall of the key principles of the IPCW method, each step of the implementation is described using a toy example. The guidelines are illustrated in a case study that aimed at evaluating the benefit of therapy based on tumour molecular profiling for advanced cancers, SHIVA01. •Handling treatment switches in the randomized clinical trial setting.•R package implementing the inverse probability of censoring weights (IPCW) method.•New R functions to facilitate data formatting.•Step-by-step tutorial to run the IPCW method.•Focus on a previously published randomized clinical trial, SHIVA01.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2019.103339