title: Inference using conditional logistic regression with missing covariates.
abstract: When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a complete-case analysis. In this article, we derive a modified conditional logistic regression that is appropriate with covariates that are missing at random. Performing a conditional logistic regression with only the complete cases is convenient with existing statistical packages, but it may give bias if missingness is not completely at random.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. stuart@jimmy.harvard.edu
authors.forename: S R
authors.initials: SR
authors.lastname: Lipsitz
authors.forename: M
authors.initials: M
authors.lastname: Parzen
authors.forename: M
authors.initials: M
authors.lastname: Ewell
mesh_terms: D015982:Bias; D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D006801:Humans; D007565:Jaundice / Q000209:etiology; D008113:Liver Neoplasms / Q000150:complications / Q000503:physiopathology; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D018401:Sample Size; D005719:gamma-Globulins / Q000378:metabolism
chemical_list: D005719:gamma-Globulins