Premium
Analyzing panel count data with a dependent observation process and a terminal event
Author(s) -
Zhao Hui,
Li Yang,
Sun Jianguo
Publication year - 2013
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11143
Subject(s) - event (particle physics) , covariate , weighting , process (computing) , statistics , conditional expectation , inverse probability weighting , econometrics , terminal (telecommunication) , count data , computer science , conditional probability distribution , mathematics , estimating equations , maximum likelihood , physics , quantum mechanics , medicine , telecommunications , propensity score matching , poisson distribution , radiology , operating system
Panel count data occur in many fields and a number of approaches have been developed. However, most of these approaches are for situations where there is no terminal event and the observation process is independent of the underlying recurrent event process unconditionally or conditional on the covariates. In this paper, we discuss a more general situation where the observation process is informative and there exists a terminal event which precludes further occurrence of the recurrent events of interest. For the analysis, a semiparametric transformation model is presented for the mean function of the underlying recurrent event process among survivors. To estimate the regression parameters, an estimating equation approach is proposed in which an inverse survival probability weighting technique is used. The asymptotic distribution of the proposed estimates is provided. Simulation studies are conducted and suggest that the proposed approach works well for practical situations. An illustrative example is provided. The Canadian Journal of Statistics 41: 174–191; 2013 © 2012 Statistical Society of Canada