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

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  • Szilárd Nemes

    (BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden
    Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden)

  • Erik Bülow

    (Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
    The Swedish Hip Arthroplasty Register, Registercentrum Västra Götaland, 41390 Gothenburg, Sweden)

  • Andreas Gustavsson

    (BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden)

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.

Suggested Citation

  • Szilárd Nemes & Erik Bülow & Andreas Gustavsson, 2020. "A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances," Stats, MDPI, vol. 3(2), pages 1-13, May.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:2:p:10-119:d:363234
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    References listed on IDEAS

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    1. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    2. Paul Meier & Theodore Karrison & Rick Chappell & Hui Xie, 2004. "The Price of Kaplan-Meier," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 890-896, January.
    3. Jane‐Ling Wang, 1999. "Asymptotic Properties of M‐estimators Based on Estimating Equations and Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(2), pages 297-318, June.
    4. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    5. Lu Tian & Hua Jin & Hajime Uno & Ying Lu & Bo Huang & Keaven M. Anderson & LJ Wei, 2020. "On the empirical choice of the time window for restricted mean survival time," Biometrics, The International Biometric Society, vol. 76(4), pages 1157-1166, December.
    6. Morten Overgaard & Per K. Andersen & Erik T. Parner, 2015. "Regression analysis of censored data using pseudo-observations: An update," Stata Journal, StataCorp LP, vol. 15(3), pages 809-821, September.
    7. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
    8. Martin Jullum & Nils Lid Hjort, 2019. "What price semiparametric Cox regression?," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 406-438, July.
    9. Erik T. Parner & Per K. Andersen, 2010. "Regression analysis of censored data using pseudo-observations," Stata Journal, StataCorp LP, vol. 10(3), pages 408-422, September.
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