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 language | English (US) |
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Pages (from-to) | 111-124 |
Number of pages | 14 |
Journal | Journal of the American Statistical Association |
Volume | 94 |
Issue number | 445 |
DOIs | |
State | Published - 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