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Mir Said Siadaty, MS MD, Division of Biostatistics and Epidemiology, University opf Virginia, UVA School of Medicine, DHES, Box 800717, Charlottesville, VA 22908, 434 982 4436, mirSiadaty@virginia.edu and Jianfen Shu, MS, Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, PO Box 19024, MP702, 1100 Fairview Avenue N., Seattle, WA 98109-1024.
Consider a meta-analysis where a head-to-head comparison of diagnostic tests for a disease of interest is intended. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. Some of the papers may have studied more than one test, hence the results are not independent. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups. We propose a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of OR0, a baseline function capturing the way OR changes across papers. The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests. We show how to expand the domain of the model to cover dependent studies, multiple outcomes, multiple thresholds, and individual-level data. In the paper we demonstrate how to formulate the model for a few real examples, and how to use widely available or popular statistical software (like SAS, R or S-Plus, and Stata) to fit the models, and estimate the discrimination accuracy of tests. Furthermore, we provide code for converting ORs into other measures of test performance like predictive values, post-test probabilities, and likelihood ratios, under mild conditions. Also we provide code to convert numerical results into graphical ones, like SROC curves, forest plots, and pre-post test probability graphs. The POR model suits the daily practice of meta-analysis and improves clinical decision-making.
Learning Objectives:
Related Web page: www.people.virginia.edu/~mss4x/por.html
Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.