Logistic regression with incompletely observed categorical covariates--investigating the sensitivity against violation of the missing at random assumption.
Missing values in the covariates are a widespread complication in the statistical inference of regression models. The maximum likelihood principle requires specification of the distribution of the covariates, at least in part. For categorical covariates, log-linear models can be used. Additionally, the missing at random assumption is necessary, which excludes a dependence of the occurrence of missing values on the unobserved covariate values. This assumption is often highly questionable. We present a framework to specify alternative missing value mechanisms such that maximum likelihood estimation of the regression parameters under a specified alternative is possible. This allows investigation of the sensitivity of a single estimate against violations of the missing at random assumption. The possible results of a sensitivity analysis are illustrated by artificial examples. The practical application is demonstrated by the analysis of two case-control studies.
Vach
W
W
Institute of Medical Biometry and Informatics, University of Freiburg, Germany.
Blettner
M
M
D000017:ABO Blood-Group System / Q000032:analysis; D000328:Adult; D000704:Analysis of Variance; D015982:Bias; D001932:Brain Neoplasms / Q000209:etiology; D001943:Breast Neoplasms / Q000209:etiology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009381:Neoplasms, Radiation-Induced / Q000209:etiology; D016609:Neoplasms, Second Primary / Q000209:etiology; D011336:Probability; D012044:Regression Analysis; D012307:Risk Factors
D000017:ABO Blood-Group System