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This experimental design, known as group testing (or pooled testing), can provide a large reduction in testing costs and can offer nearly the same precision as individual testing. To account for covariate information on individual subjects, regression models for group testing data have been proposed recently. However, there are currently no tools available to check the adequacy of these models. In this paper, we present various global goodness\u2010of\u2010fit tests for regression models with group testing data. We use simulation to examine the small\u2010sample size and power properties of the tests for different pool composition strategies. We illustrate our methods using two infectious disease data sets, one from an <jats:sc>HIV<\/jats:sc> study in Kenya and one from the Infertility Prevention Project. 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