Semiparametric regression analysis of panel count data allowing for within-subject correlation

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• 作者：Bin Yao^a ; ^{yaob@email.sc.edu" class="auth_mail" title="E-mail the corresponding author} ; Lianming Wang^a ; Xin He^b
• 关键词：EM algorithm ; Gamma frailty ; Monotone splines ; Panel count data ; Poisson process
• 刊名：Computational Statistics & Data Analysis
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：97
• 期：Complete
• 页码：47-59
• 全文大小：484 K

文摘

In this paper, a maximum likelihood approach is proposed for analyzing panel count data under the gamma frailty non-homogeneous Poisson process model. The approach allows one to estimate the baseline mean function and the regression parameters jointly while taking the within-subject correlation into account. The within-subject correlation is quantified explicitly by Pearson’s correlation coefficient. Monotone splines are adopted to approximate the unspecified nondecreasing baseline mean function in the model. An expectation–maximization (EM) algorithm is derived to facilitate the computation by exploiting a data augmentation based on Poisson latent variables. The EM algorithm is robust to initial values, easy to implement, converges fast, and provides closed-form variance estimates. It can be also applied to the non-homogeneous Poisson model without frailty. The proposed approach is evaluated through simulations and illustrated by two real-life examples coming from a skin cancer study and a bladder tumor study. A companion R package PCDSpline has been developed and is available on R CRAN for public use.