Extended Rank Tests for Analyzing Recurrent Event Data. (2nd January 2020)

• Record Type: Journal Article
• Authors: Zhao, Qiang; Zhang, Bin; LaValley, Michael P.; Massaro, Joseph M.; Lunetta, Kathryn L.; Chang, Mark
• Is Part Of: Statistics in biopharmaceutical research. Volume 12:Number 1(2020)
• Page Start: 90
• Page End: 98
• Publication Date: 2020-01-02
• Subjects: Bias -- Informative number of events -- Intra-subject correlation -- Rank tests -- Recurrent events; Pharmacy -- Statistical methods -- Periodicals; Pharmaceutical biotechnology -- Statistical methods -- Periodicals; Biopharmaceutics -- Periodicals; Biometry -- Periodicals; Pharmacy -- Statistical methods; Periodicals; 615.190727
• DOI: 10.1080/19466315.2019.1601596
• Languages: English
• ISSNs: 1946-6315

Abstract

Abstract: Wang and Chang studied the bias in the estimation of the marginal survival curve of recurrent event data and came up with an unbiased Kaplan–Meier (KM)-like estimator. However, there were no corresponding hypothesis tests to compare Wang and Chang's survival estimates among different groups. In this article, we extended three commonly used rank tests to compare Wang and Chang's KM-like survival estimates. Intra-subject correlation (ISC) issue is handled by using a robust variance estimator. We also studied the empirical power difference between our new method and Jung and Jeong's method which was developed for clustered survival data and explored the relationship between ISC and the power of different rank tests. Supplementary materials for this article are available online.