title: Estimating standardized parameters from generalized linear models.
abstract: Although the traditional unrestricted ('non-parametric') estimators of directly standardized rates and rate differences remain unbiased in sparse data, they tend to suffer from instability (low precision). As a result, many authors have proposed more precise estimators based on parametric models for the rates. This paper provides a general approach for constructing estimators of standardized parameters using generalized linear models, and shows that, in some common special cases, these model-based ('smoothed') estimators can have an exceptionally simple form.
authors.affiliation: Department of Epidemiology, UCLA School of Public Health 90024-1772.
authors.forename: S
authors.initials: S
authors.lastname: Greenland
mesh_terms: D000328:Adult; D000368:Aged; D001151:Arsenic / Q000009:adverse effects; D016022:Case-Control Studies; D006801:Humans; D016014:Linear Models; D008669:Metallurgy; D008875:Middle Aged; D009784:Occupational Diseases / Q000139:chemically induced / Q000401:mortality; D012044:Regression Analysis; D012142:Respiratory Tract Neoplasms / Q000139:chemically induced / Q000401:mortality; D015996:Survival Rate
chemical_list: D001151:Arsenic