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

Wenxin Jiang*, Bruce W. Turnbull, Larry C. Clark

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)111-124
Number of pages14
JournalJournal of the American Statistical Association
Volume94
Issue number445
DOIs
StatePublished - Mar 1 1999

Keywords

  • Consistency
  • Cox model
  • Estimating equations
  • Frailty
  • Measurement error
  • Omitted covariates
  • Point process
  • Poisson regression
  • Proportional intensities
  • Robust estimator
  • Selenium
  • Skin cancer
  • Specification analysis
  • Unobserved heterogeneity
  • Validation data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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