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Simulating Duration Data for the Cox Model

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  • Harden, Jeffrey J.
  • Kropko, Jonathan

Abstract

The Cox proportional hazards model is a popular method for duration analysis that is frequently the subject of simulation studies. However, no standard method exists for simulating durations directly from its data generating process because it does not assume a distributional form for the baseline hazard function. Instead, simulation studies typically rely on parametric survival distributions, which contradicts the primary motivation for employing the Cox model. We propose a method that generates a baseline hazard function at random by fitting a cubic spline to randomly drawn points. Durations drawn from this function match the Cox model’s inherent flexibility and improve the simulation’s generalizability. The method can be extended to include time-varying covariates and non-proportional hazards.

Suggested Citation

  • Harden, Jeffrey J. & Kropko, Jonathan, 2019. "Simulating Duration Data for the Cox Model," Political Science Research and Methods, Cambridge University Press, vol. 7(4), pages 921-928, October.
    • Handle: RePEc:cup:pscirm:v:7:y:2019:i:04:p:921-928_00
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    Cited by:

    1. Lore Zumeta-Olaskoaga & Maximilian Weigert & Jon Larruskain & Eder Bikandi & Igor Setuain & Josean Lekue & Helmut Küchenhoff & Dae-Jin Lee, 2023. "Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 101-126, March.
    2. Kaitlyn Cook & Wenbin Lu & Rui Wang, 2023. "Marginal proportional hazards models for clustered interval‐censored data with time‐dependent covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 1670-1685, September.
    3. Joan Barceló & Robert Kubinec & Cindy Cheng & Tiril Høye Rahn & Luca Messerschmidt, 2022. "Windows of repression: Using COVID-19 policies against political dissidents?," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(1), pages 73-89, January.
    4. Luciana Carla Chiapella & Marta Beatriz Quaglino & María Eugenia Mamprin, 2023. "Properties of the Estimators of the Cox Regression Model with Imputed Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 330-352, July.

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