title: Exact logistic regression: theory and examples.
abstract: We provide an alternative to the maximum likelihood method for making inferences about the parameters of the logistic regression model. The method is based appropriate permutational distributions of sufficient statistics. It is useful for analysing small or unbalanced binary data with covariates. It also applies to small-sample clustered binary data. We illustrate the method by analysing several biomedical data sets.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, USA.
authors.forename: C R
authors.initials: CR
authors.lastname: Mehta
authors.forename: N R
authors.initials: NR
authors.lastname: Patel
mesh_terms: D000465:Algorithms; D000700:Analgesics / Q000494:pharmacology; D001145:Arrhythmias, Cardiac / Q000139:chemically induced; D016516:CD4-CD8 Ratio; D016022:Case-Control Studies; D016001:Confidence Intervals; D018592:Cross-Over Studies; D018572:Disease-Free Survival; D005260:Female; D015658:HIV Infections / Q000276:immunology; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D007744:Obstetric Labor Complications; D012516:Osteosarcoma / Q000401:mortality; D011247:Pregnancy; D012107:Research Design; D018401:Sample Size; D012559:Schizophrenia / Q000503:physiopathology; D019966:Substance-Related Disorders / Q000188:drug therapy
chemical_list: D000700:Analgesics