Optimal dynamic treatment regimes with survival endpoints: introducing DWSurv in the R package DTRreg. Issue 16 (1st November 2020)

• Record Type: Journal Article
• Authors: Simoneau, Gabrielle; Moodie, Erica E. M.; Wallace, Michael P.; Platt, Robert W.
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 16(2020)
• Page Start: 2991
• Page End: 3008
• Publication Date: 2020-11-01
• Subjects: Accelerated failure time -- causal inference -- censored data -- dynamic weighted ordinary least squares -- dynamic weighted survival modeling -- precision medicine; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1793341
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Precision medicine is an approach to health care in which treatment decisions are tailored to patient-level information. Statistical methods for the estimation of dynamic treatment regimes (DTRs) allow to uncover a sequence of personalized treatment rules for patients with chronic diseases. Of particular interest is the identification of an optimal DTR, that is, the sequence of treatment rules that yields the best expected outcome. This is a challenging task, especially when the outcome is a survival time subject to right censoring or when available data are from observational studies. Dynamic weighted survival modelling (DWSurv) has been demonstrated to be theoretically robust and is accessible to users. We describe its implementation using the DWSurv function in the R package DTRreg . We review on the theory underlying DWSurv and demonstrate its use with hypothetical, and real-life inspired, simulated data sets.