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Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method

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  • Hani M. Samawi
  • Lili Yu
  • Haresh Rochani
  • Robert Vogel

Abstract

In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.

Suggested Citation

  • Hani M. Samawi & Lili Yu & Haresh Rochani & Robert Vogel, 2020. "Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(6), pages 1281-1298, March.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:6:p:1281-1298
    DOI: 10.1080/03610926.2018.1554141
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    Cited by:

    1. Zeinab Akbari Ghamsari & Ehsan Zamanzade & Majid Asadi, 2024. "Using nomination sampling in estimating the area under the ROC curve," Computational Statistics, Springer, vol. 39(5), pages 2721-2742, July.
    2. Ehsan Zamanzade & M. Mahdizadeh & Hani M. Samawi, 2020. "Efficient estimation of cumulative distribution function using moving extreme ranked set sampling with application to reliability," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 485-502, September.

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