Abstract #301315

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JSM 2003 Abstract #301315
Activity Number: 18
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #301315
Title: A Permutation Test for Inference in Logistic Regression with Small Datasets
Author(s): Douglas M. Potter*+
Companies: University of Pittsburgh
Address: 1247 Northwestern Dr., Monroeville, PA, 15146-4403,
Keywords: permutation test ; logistic regression ; small datasets
Abstract:

Inference based on large sample results can be highly inaccurate if applied to logistic regression with small datasets. Furthermore, if the values of the independent variables for one outcome do not sufficiently overlap those for the alternative outcome, logistic regression procedures will fail to converge, and large sample results will be invalid. Exact conditional logistic regression can always be used, but can be too conservative; this approach also generally requires grouping the values of continuous variables corresponding to nuisance parameters, and inference can depend on how this is done. A permutation test of the hypothesis that a regression parameter is zero can overcome these limitations.The variable of interest is replaced by the residuals from a linear regression of it on all other independent variables. Logistic regressions are then done for permutations of these residuals, and a p value is computed by comparing the resulting likelihood ratio statistics to the original observed value. Simulations show that Type I error is well-controlled in a variety of situations and that performance is usually similar to that of exact conditional logistic regression.

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