Likelihood Methods for Missing Covariate Data in Highly Stratified Studies

DOI10.1111/1467-9868.00411zbMath1063.62102OpenAlexW1988484017MaRDI QIDQ4672187

Paul J. Rathouz

Publication date: 29 April 2005

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9868.00411

zbMATH Keywords

fixed effectsnuisance parametermissing datasemiparametric efficiencyconditional logistic regressionconditional likelihoodmatched case-control

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Point estimation (62F10) Generalized linear models (logistic models) (62J12)

Related Items (7)

A new semiparametric procedure for matched case-control studies with missing covariates ⋮ Empirical and weighted conditional likelihoods for matched case-control studies with missing covariates ⋮ Missing Exposure Data in Stereotype Regression Model: Application to Matched Case-Control Study with Disease Subclassification ⋮ Latent class based multiple imputation approach for missing categorical data ⋮ A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case-Control Studies ⋮ Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure ⋮ Handling missing data in matched case‐control studies using multiple imputation

Cites Work

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• Conditional and Unconditional Categorical Regression Models with Missing Covariates
• Informative Drop-Out in Longitudinal Data Analysis
• Matched Case–Control Data Analyses with Missing Covariates

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