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On Cox proportional hazards model performance under different sampling schemes

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  • Hani Samawi
  • Lili Yu
  • JingJing Yin

Abstract

Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on simple random sampling (SRS). We also showed theoretically that Fisher’s information for DERSS is higher than that of ERSS, and ERSS is higher than SRS. We used the SEER Incidence Data for illustration. Our proposed methods are cost saving sampling schemes.

Suggested Citation

  • Hani Samawi & Lili Yu & JingJing Yin, 2023. "On Cox proportional hazards model performance under different sampling schemes," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-15, April.
    • Handle: RePEc:plo:pone00:0278700
    DOI: 10.1371/journal.pone.0278700
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    References listed on IDEAS

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    1. Usman Shahzad & Ishfaq Ahmad & Evrim Oral & Muhammad Hanif & Ibrahim Mufrah Almanjahie, 2021. "Estimation of the population mean by successive use of an auxiliary variable in median ranked set sampling," Mathematical Population Studies, Taylor & Francis Journals, vol. 28(3), pages 176-199, July.
    2. Shashi Bhushan & Anoop Kumar & Sana Shahab & Showkat Ahmad Lone & Md Tanwir Akhtar & Jia-Bao Liu, 2022. "On Efficient Estimation of the Population Mean under Stratified Ranked Set Sampling," Journal of Mathematics, Hindawi, vol. 2022, pages 1-20, October.
    3. Hani M. Samawi & Haresh Rochani & Daniel Linder & Arpita Chatterjee, 2017. "More efficient logistic analysis using moving extreme ranked set sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 753-766, March.
    4. Shashi Bhushan & Anoop Kumar & Sana Shahab & Showkat Ahmad Lone & Salemah A. Almutlak, 2022. "Modified Class of Estimators Using Ranked Set Sampling," Mathematics, MDPI, vol. 10(21), pages 1-13, October.
    5. Shashi Bhushan & Anoop Kumar & Usman Shahzad & Amer Ibrahim Al-Omari & Ibrahim Mufrah Almanjahie, 2022. "On Some Improved Class of Estimators by Using Stratified Ranked Set Sampling," Mathematics, MDPI, vol. 10(18), pages 1-32, September.
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