A weighted least squares statistic is commonly used to test homogeneity of the risk difference for a series of tables. Since the method is based on asymptotic theory, its type I error rate is inflated when the data are sparse. Two new methods for testing the homogeneity of risk difference across different groups in clinical trials are proposed in this paper. These methods are constructed, based on the Wilson's score test and traditional weighted least squares statistics. The performance of the new methods is evaluated and compared to the currently available approaches. Results show that one of our new methods has a type I error rate that is closest to the nominal level among all the methods and is much more powerful than those proposed by Lipsitz et al.
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