title: Standardized estimates from categorical regression models.
abstract: We consider the problem of interpreting categorical regression models, such as the polytomous logistic model, the continuation-ratio model, the stereotype model, and the cumulative-odds model. We present a method to convert categorical regression coefficients into estimates of standardized fitted probabilities, probability differences and probability ratios. We use a delta-method approach to estimate standard errors. We then present a small simulation study to compare different transforms for setting confidence limits, and provide an illustration of our approach in an observational study of drug therapy of polymyositis.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: M M
authors.initials: MM
authors.lastname: Joffe
authors.forename: S
authors.initials: S
authors.lastname: Greenland
mesh_terms: D000465:Algorithms; D000893:Anti-Inflammatory Agents / Q000276:immunology; D001323:Autoantibodies / Q000276:immunology; D001379:Azathioprine / Q000276:immunology; D016001:Confidence Intervals; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D007166:Immunosuppressive Agents / Q000276:immunology; D015233:Models, Statistical; D016017:Odds Ratio; D017285:Polymyositis / Q000188:drug therapy; D011241:Prednisone / Q000276:immunology; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016896:Treatment Outcome
chemical_list: D000893:Anti-Inflammatory Agents; D001323:Autoantibodies; D007166:Immunosuppressive Agents; D001379:Azathioprine; D011241:Prednisone