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 Georgia Southern researchers propose a more efficient sampling method of recruiting subjects for survival analysis. Researchers 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.
“Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method” was recently published in Communications in Statistics—Theory and Methods
Authors are Dr. Hani M. Samawi. Dr. Lili Yu, Dr. Haresh Rochani, and Dr. Robert Vogel at the Georgia Southern University Jiann-Ping Hsu College of Public Health.