title: A solution to the problem of separation in logistic regression.
abstract: The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to +/- infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald tests and confidence intervals are available but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. The clear advantage of the procedure over previous options of analysis is impressively demonstrated by the statistical analysis of two cancer studies.
authors.affiliation: Section of Clinical Biometrics, Department of Medical Computer Sciences, University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria. georg.heinz@akh-wien.ac.at
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
authors.forename: Michael
authors.initials: M
authors.lastname: Schemper
mesh_terms: D001943:Breast Neoplasms / Q000532:radiotherapy; D016022:Case-Control Studies; D003198:Computer Simulation; D016889:Endometrial Neoplasms / Q000473:pathology; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008175:Lung Neoplasms / Q000209:etiology; D009010:Monte Carlo Method; D009389:Neovascularization, Pathologic / Q000473:pathology; D011878:Radiotherapy / Q000009:adverse effects; D012907:Smoking / Q000009:adverse effects