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Sampling for Conditional Inference on Case–Control Data
Author(s) -
Chen Yuguo,
Dinwoodie Ian H.,
MacGibbon Brenda
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00763.x
Subject(s) - conditional probability distribution , inference , sampling (signal processing) , covariate , mathematics , poisson distribution , statistics , computer science , algorithm , artificial intelligence , filter (signal processing) , computer vision
Summary 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.