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The examination time process can be informative about the underlying recurrent event process even after conditioning on covariates. We consider a semiparametric accelerated mean model for the recurrent event process and allow the two processes to be correlated through a shared frailty. The regression parameters have a simple marginal interpretation of modifying the time scale of the cumulative mean function of the event process. A novel estimation procedure for the regression parameters and the baseline rate function is proposed based on a conditioning technique. In contrast to existing methods, the proposed method is robust in the sense that it requires neither the strong Poisson-type assumption for the underlying recurrent event process nor a parametric assumption on the distribution of the unobserved frailty. Moreover, the distribution of the examination time process is left unspecified, allowing for arbitrary dependence between the two processes. Asymptotic consistency of the estimator is established, and the variance of the estimator is estimated by a model-based smoothed bootstrap procedure. Numerical studies demonstrated that the proposed point estimator and variance estimator perform well with practical sample sizes. The methods are applied to data from a skin cancer chemoprevention trial.<\/jats:p>","DOI":"10.1111\/biom.12840","type":"journal-article","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T15:06:44Z","timestamp":1514560004000},"page":"944-953","source":"Crossref","is-referenced-by-count":13,"title":["Semiparametric Estimation of the Accelerated Mean Model with Panel Count Data Under Informative Examination Times"],"prefix":"10.1093","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2672-662X","authenticated-orcid":false,"given":"Sy Han","family":"Chiou","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, University of Texas at Dallas , Richardson, Texas 75080 , U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongjun","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Michigan , Ann Arbor, Michigan 48109 , 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