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. Accelerated failure time (AFT) models provide a linear relationship between the log of the failure time and covariates that affect the expected time to failure by contracting or expanding the time scale. The AFT model has intensive application in the field of social, medical, behavioral, and public health sciences. In this article researchers propose a more efficient sampling method of recruiting subjects for survival analysis. They propose using a Moving Extreme Ranked Set Sampling (MERSS) or an Extreme Ranked Set Sampling (ERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This article demonstrates that these approaches provide 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 Health Study data are used to illustrate the methods developed in this article.
“Reducing sample size needed for accelerated failure time model using more efficient sampling methods,” was recently published in the Journal of Statistical Theory and Practice.
Dr. Hani Samawi, professor of biostatistics at the Georgia Southern University Jiann-Ping Hsu College of Public Health was the lead author. Dr. Amal Helu, University of Jordan, and Drs. Haresh Rochani, JinJing Yin, Lili Yu, and Robert Vogel, professors of biostatistics at the JPHCOPH were co-authors.