Estimating Equations with Incomplete Categorical Covariates in the Cox Model

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. When a full likelihood is specified, a useful technique for obtaining parameter estimates is the EM algorithm. We propose a set of estimating equations to estimate the parameters of Cox's pro...

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Bibliographic Details
Published inBiometrics Vol. 54; no. 3; pp. 1002 - 1013
Main Authors Lipsitz, Stuart R., Ibrahim, Joseph G.
Format Journal Article
LanguageEnglish
Published United States International Biometric Society 01.09.1998
Subjects
Algorithms
Biometrics
Biometry - methods
Censorship
Clinical Trials as Topic - statistics & numerical data
Data Interpretation, Statistical
Datasets
Estimation bias
Estimation methods
Humans
Liver Neoplasms - mortality
Maximum likelihood estimation
Missing data
Monte Carlo Method
Monte Carlo methods
Parametric models
Proportional Hazards Models
Regression Analysis
Statistical discrepancies
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Summary:Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. When a full likelihood is specified, a useful technique for obtaining parameter estimates is the EM algorithm. We propose a set of estimating equations to estimate the parameters of Cox's proportional hazards model when some covariate values are missing. These estimating equations can be solved by an algorithm similar to the EM algorithm. Because of the computational burden of finding a solution to these estimating equations, we propose obtaining parameter estimates via Monte Carlo methods. Asymptotic variances of the parameter estimates are also derived. We present a clinical trials example with three covariates, two of which have some missing values.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0006-341X
1541-0420
DOI:10.2307/2533852