Statistical inference methods for recurrent event processes with shape and size parameters

• 成果类型: Article
• 署名作者: Wang, Mei-Cheng; Huang, Chiung-Yu
• 署名单位: Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu016
• 发表日期: 2014
• 页码: 553566
• 关键词: SEMIPARAMETRIC ANALYSIS; regression; times

摘要

This paper proposes a unified framework to characterize the rate function of a recurrent event process through shape and size parameters. In contrast to the intensity function, which is the event occurrence rate conditional on the event history, the rate function is the occurrence rate unconditional on the event history, and thus it can be interpreted as a population-averaged count of events in unit time. In this paper, shape and size parameters are introduced and used to characterize the association between the rate function lambda(.) and a random variable X. Measures of association between X and lambda(.) are defined via shape- and size-based coefficients. Rate-independence of X and lambda(.) is studied through tests of shape-independence and size-independence, where the shapeand size-based test statistics can be used separately or in combination. These tests can be applied when X is a covariable possibly correlated with the recurrent event process through lambda(.) or, in the one-sample setting, when X is the censoring time at which the observation of N(.) is terminated. The proposed tests are shape- and size-based, so when a null hypothesis is rejected, the test results can serve to distinguish the source of violation.