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

Wenxin Jiang; Bruce W. Turnbull; Larry C. Clark

doi:10.1080/01621459.1999.10473828

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

Statistics and Data Science

Research output: Contribution to journal › Article › peer-review

20 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 language	English (US)
Pages (from-to)	111-124
Number of pages	14
Journal	Journal of the American Statistical Association
Volume	94
Issue number	445
DOIs	https://doi.org/10.1080/01621459.1999.10473828
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/01621459.1999.10473828

Cite this

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title = "Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error",

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.",

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",

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doi = "10.1080/01621459.1999.10473828",

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T1 - Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error

AU - Jiang, Wenxin

AU - Turnbull, Bruce W.

AU - Clark, Larry C.

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Y1 - 1999/3/1

N2 - 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.

AB - 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.

KW - Consistency

KW - Cox model

KW - Estimating equations

KW - Frailty

KW - Measurement error

KW - Omitted covariates

KW - Point process

KW - Poisson regression

KW - Proportional intensities

KW - Robust estimator

KW - Selenium

KW - Skin cancer

KW - Specification analysis

KW - Unobserved heterogeneity

KW - Validation data

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DO - 10.1080/01621459.1999.10473828

M3 - Article

AN - SCOPUS:0442309486

SN - 0162-1459

VL - 94

SP - 111

EP - 124

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 445

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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