Conditional and Unconditional Categorical Regression Models with Missing Covariates

DOI10.1111/J.0006-341X.2000.00384.XzbMath1060.62662WikidataQ73946344 ScholiaQ73946344MaRDI QIDQ4667453

Glen A. Satten, Raymond J. Carroll

Publication date: 20 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

likelihoodmatchingmissing datamissing at randomcase-control studyendometrial cancertwo-stage sample

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (16)

Nonignorable Missingness in Matched Case-Control Data Analyses ⋮ 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 ⋮ Analysis of local sensitivity to nonignorability with missing outcomes and predictors ⋮ Missing Exposure Data in Stereotype Regression Model: Application to Matched Case-Control Study with Disease Subclassification ⋮ Multiple Imputation forM-Regression With Censored Covariates ⋮ A Note on the Prospective Analysis of Outcome-Dependent Samples ⋮ Likelihood Methods for Missing Covariate Data in Highly Stratified Studies ⋮ 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 ⋮ A Semiparametric Odds Ratio Model for Measuring Association ⋮ Handling missing data in matched case‐control studies using multiple imputation ⋮ Semi-parametric Bayesian analysis of binary responses with a continuous covariate subject to non-random missingness ⋮ An estimated-score approach for dealing with missing covariate data in matched case-control studies ⋮ Semiparametric Regression Splines in Matched Case‐Control Studies ⋮ Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States

Cites Work

• Estimation in choice-based sampling with measurement error and bootstrap analysis
• Conditional Regression Analysis of the Exposure-Disease Odds Ratio Using Known Probability-of-Exposure Values
• Inferences About Exposure-Disease Associations Using Probability-of- Exposure Information
• Matched Case–Control Data Analyses with Missing Covariates

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