title: Sampling for conditional inference on case-control data.
abstract: The problem of exact conditional inference for discrete multivariate case-control data has two forms. The first is grouped case-control data, where Monte Carlo computations can be done using the importance sampling method of Booth and Butler (1999, Biometrika86, 321-332), or a proposed alternative sequential importance sampling method. The second form is matched case-control data. For this analysis we propose a new exact sampling method based on the conditional-Poisson distribution for conditional testing with one binary and one integral ordered covariate. This method makes computations on data sets with large numbers of matched sets fast and accurate. We provide detailed derivation of the constraints and conditional distributions for conditional inference on grouped and matched data. The methods are illustrated on several new and old data sets.
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, USA. yuguo@uiuc.edu
authors.forename: Yuguo
authors.initials: Y
authors.lastname: Chen
authors.forename: Ian H
authors.initials: IH
authors.lastname: Dinwoodie
authors.forename: Brenda
authors.initials: B
authors.lastname: MacGibbon
mesh_terms: D001699:Biometry / Q000379:methods; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D018401:Sample Size