Exact Inference Software for Correlated Categorical Data

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DESCRIPTION (provided by applicant): This is a Phase I SBIR proposal for the development of computer software performing small sample exact inference for correlated categorical data. Such data are now common in many, biomedical areas of research, such as genetics, ophthalmology, and teratology. In addition, exact methods are not available in any commercial package and are badly needed for accurate inference. By the end of Phase II we plan to develop tools for analyzing small as well as large data sets with correlated binary and correlated multivariate responses. This set of tools will compute point estimates and confidence intervals, as well as perform hypothesis tests, for several likelihood-based models for a multivariate binary response. In this Phase I effort, we will 1. build a stand-alone program with a simple user interface, including a data editor and a menu to specify fixed covariates and correlation structure of the data; 2. implement within this computer program a) an exact trend test for stratified ordered clustered binomial populations, b) an exact procedure to test for clustering c) an exact trend test for multiple outcome data In addition, we will investigate the feasibility of network-based Monte Carlo methods for fitting such models. Because there exists no commercial statistical software for such methods for exact inference, the resulting module will fill an important gap in statistical tools for categorical data analysis, and will be incorporated into new versions of the StatXact and LogXact, Cytel's flagship software packages, we shall also implement all these tools in widely used statistical package SAS as a SAS procedure.