Inference Using Conditional Logistic Regression with Missing Covariates

DOI10.2307/2534015zbMath1058.62542OpenAlexW2128449284WikidataQ74449051 ScholiaQ74449051MaRDI QIDQ4665960

Michael Parzen, Marian Ewell, Stuart R. Lipsitz

Publication date: 11 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2534015

zbMATH Keywords

nuisance parametersmissing at randommissing completely at randomcomplete-case analysismissing covariate data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

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Robust logistic regression of family data in the presence of missing genotypes ⋮ Nonignorable Missingness in Matched Case-Control Data Analyses ⋮ A new semiparametric procedure for matched case-control studies with missing covariates ⋮ Conditional logistic regression in cluster-specific1:mmatched treatment–control designs ⋮ Empirical and weighted conditional likelihoods for matched case-control studies with missing covariates ⋮ Analysis of local sensitivity to nonignorability with missing outcomes and predictors ⋮ Estimation of logistic regression with covariates missing separately or simultaneously via multiple imputation methods ⋮ Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models ⋮ A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case-Control Studies ⋮ Handling missing data in matched case‐control studies using multiple imputation ⋮ Missing covariates in logistic regression, estimation and distribution selection ⋮ Performances of Bayesian model selection criteria for generalized linear models with non-ignorably missing covariates ⋮ 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

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