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Journal of the American Statistical Association Volume 94, 1999 - Issue 445
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Theory and Method

Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error

Wenxin Jiang Department of Statistics, Northwestern University, Evanston, IL, 60208
,
Bruce W. Turnbull School of Operations Research and Department of Statistical Science, Cornell University, Ithaca, NY, 14853
&
Larry C. Clark Arizona Cancer Center, University of Arizona, Tucson, AZ, 85724
Pages 111-124 | Received 01 Feb 1997, Published online: 17 Feb 2012
 
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Abstract

Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our approach to parameter estimation and significance testing is to start with a naive model of semiparametric Poisson process regression, and then to adjust for random effects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical trial for the prevention of recurrent skin tumors.

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