Sampling for conditional inference on case-control data

Yuguo Chen, Ian H. Dinwoodie, Brenda MacGibbon

Research output: Contribution to journalArticlepeer-review


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, Biometrika 86, 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.

Original languageEnglish (US)
Pages (from-to)845-855
Number of pages11
Issue number3
StatePublished - Sep 2007


  • Case-control
  • Conditional Poisson
  • Contingency table
  • Matched study
  • Retrospective study
  • Sequential importance sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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