A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances

Szilárd Nemes, Erik Bülow and Andreas Gustavsson
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Szilárd Nemes: BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden
Erik Bülow: Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
Andreas Gustavsson: BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden

Stats, 2020, vol. 3, issue 2, 1-13

Abstract: Restricted Mean Survival Time ( R M S T ) experiences a renaissance and is advocated as a model-free, easy to interpret alternative to proportional hazards regression and hazard rates with implication in causal inference. Estimation of R M S T and associated variance is mainly done by numerical integration of Kaplan–Meier curves. In this paper we briefly review the two main alternatives to the Kaplan–Meier method; analysis based on pseudo-observations, and the flexible parametric survival method. Using computer simulations, we assess the efficacy of the three methods compared to a fully parametric approach where the distribution of survival times is known. Thereafter, the three methods are directly compared without any distributional assumption for the survival data. Generally, flexible parametric survival methods outperform both competitors, however the differences are small.

Keywords: RMST; censoring; variance estimator; efficacy; Kaplan–Meier; flexible-survival methods; pseudo-observation (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
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