Marginal analysis of panel counts through estimating functions

• 成果类型: Article
• 署名作者: Hu, X. Joan; Lagakos, Stephen W.; Lockhart, Richard A.
• 署名单位: Simon Fraser University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp010
• 发表日期: 2009
• 页码: 445456
• 关键词: FAILURE TIME DATA; recurrent events; nonparametric-estimation; regression-analysis; Poisson regression; point-processes; Mean Function; em algorithm; models; likelihood

摘要

We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied by Wellner & Zhang (2000), assuming a Poisson counting process. It gives a nondecreasing estimator, which equals the nonparametric maximum likelihood estimator of Wellner & Zhang and is consistent without the Poisson assumption. Motivated by the construction of parametric generalized estimating equations, the second type is a set of data-adaptive quasi-score functions, which are likelihood estimating functions under a mixed-Poisson assumption. We evaluate the procedures using simulation, and illustrate them with the data from a bladder cancer study.