Exact Logistic Regression for a Matched Pairs case-Control Design with Polytomous Exposure Variables
Abstract
Logistic regression methods are useful in estimating odds ratios under matched pairs case-control designs when the exposure variable of interest is binary or polytomous in nature. Analysis is typically performed using large sample approximation techniques. When conducting the analysis with polytomous exposure variable, situations where the numbers of discordant pairs in the resulting cells are small or the data structure is sparse can be encountered. In such situations, the asymptotic method of analysis is questionable, thus an exact method of analysis may be more suitable. A method is presented that performs exact inference in the case of pair-wise matched case-control data with more than two unordered exposure categories using a distribution of conditional sufficient statistics of logistic model parameters.
DOI
10.22237/jmasm/1351743300
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
