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Modified Cox Models: A Simulation Study on Different Survival Distributions, Censoring Rates, and Sample Sizes

Author

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  • Iketle Aretha Maharela

    (Department of Statistics, University of Pretoria, Pretoria 0028, South Africa)

  • Lizelle Fletcher

    (Department of Statistics, University of Pretoria, Pretoria 0028, South Africa)

  • Ding-Geng Chen

    (Department of Statistics, University of Pretoria, Pretoria 0028, South Africa
    College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA)

Abstract

The classical Cox model is the most popular procedure for studying right-censored data in survival analysis. However, it is based on the fundamental assumption of proportional hazards (PH). Modified Cox models, stratified and extended, have been widely employed as solutions when the PH assumption is violated. Nevertheless, prior comparisons of the modified Cox models did not employ comprehensive Monte-Carlo simulations to carry out a comparative analysis between the two models. In this paper, we conducted extensive Monte-Carlo simulation to compare the performance of the stratified and extended Cox models under varying censoring rates, sample sizes, and survival distributions. Our results suggest that the models’ performance at varying censoring rates and sample sizes is robust to the distribution of survival times. Thus, their performance under Weibull survival times was comparable to that of exponential survival times. Furthermore, we found that the extended Cox model outperformed other models under every combination of censoring, sample size and survival distribution.

Suggested Citation

  • Iketle Aretha Maharela & Lizelle Fletcher & Ding-Geng Chen, 2024. "Modified Cox Models: A Simulation Study on Different Survival Distributions, Censoring Rates, and Sample Sizes," Mathematics, MDPI, vol. 12(18), pages 1-10, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2903-:d:1480291
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    References listed on IDEAS

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    1. Ronghui Xu & Yunjun Luo & Robert Glynn & Diana Johnson & Kenneth L. Jones & Christina Chambers, 2014. "Time-Dependent Propensity Score for Assessing the Effect of Vaccine Exposure on Pregnancy Outcomes through Pregnancy Exposure Cohort Studies," IJERPH, MDPI, vol. 11(3), pages 1-12, March.
    2. Zheng, Rui & Wang, Jingjing & Zhang, Yingzhi, 2023. "A hybrid repair-replacement policy in the proportional hazards model," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1011-1021.
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