A modified partial likelihood score method for Cox regression with covariate error under the internal validation design

David M. Zucker; Xin Zhou; Xiaomei Liao; Yi Li; Donna Spiegelman

doi:10.1111/biom.13012

A modified partial likelihood score method for Cox regression with covariate error under the internal validation design

David M. Zucker^*, Xin Zhou, Xiaomei Liao, Yi Li, Donna Spiegelman

^*Corresponding author for this work

Department of Statistics

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

3 Scopus citations

Abstract

We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. In a simulation study, the method was found to perform very well in terms of bias reduction and confidence interval coverage. The method is applied to data from the Health Professionals Follow-Up Study (HPFS) on the effect of diet on incidence of Type II diabetes.

Original language	English
Pages (from-to)	414-427
Number of pages	14
Journal	Biometrics
Volume	75
Issue number	2
DOIs	https://doi.org/10.1111/biom.13012
State	Published - 2019

Bibliographical note

Publisher Copyright:
© 2019 International Biometric Society

Keywords

Cox model
measurement error
modified score

UN SDGs

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

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10.1111/biom.13012

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AU - Spiegelman, Donna

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A modified partial likelihood score method for Cox regression with covariate error under the internal validation design

Abstract

Bibliographical note

Keywords

UN SDGs

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