Treatment Switching
Implements rank-preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), and two-stage estimation (TSE) methods for treatment switching in randomized clinical trials.
To enable bootstrapping of the parameter estimates, we implements many standard survival analysis methods in C++. These include but are not limited to Kaplan-Meier estimates of the survival curves, log-rank tests, accelerate failure time models, and Cox proportional hazards models.
All treatment switching adjustment methods allow treatment switching in both treatment arms, stratification and covariates adjustment. For the AFT models, stratification factors are included as covariates (main effects only or all-way interactions) because SAS PROC LIFEREG does not have the strata statement. The RPSFTM, IPE and TSE methods can be used with or without recensoring. The IPCW method can produce stabilized and truncated weights.
The treat variable adopts a treatment-before-control order, except with 1/0 or TRUE/FALSE coding.
James M. Robins and Anastasios A. Tsiatis. Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Communications in Statistics. 1991;20(8):2609-2631.
Ian R. White, Adbel G. Babiker, Sarah Walker, and Janet H. Darbyshire. Randomization-based methods for correcting for treatment changes: Examples from the CONCORDE trial. Statistics in Medicine. 1999;18:2617-2634.
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Ian R White. Letter to the Editor: Estimating treatment effects in randomized trials with treatment switching. Statistics in Medicine. 2006;25:1619-1622.
James M. Robins and Dianne M. Finkelstein. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics. 2000;56:779-788.
Nicholas R. Latimer, Keith R. Abrams, Paul C. Lambert, Michael K. Crowther, Allan J. Wailoo, Jonathan P. Morden, Ron L. Akehurst, and Michael J. Campbell. Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method. Statistical Methods in Medical Research. 2017;26(2):724-751.
Nicholas R. Latimer, Ian R. White, Kate Tilling, and Ulrike Siebert. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding. Statistical Methods in Medical Research. 2020;29(10):2900-2918.
Kaifeng Lu, kaifenglu@gmail.com
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