Speaker: Xiaofang Yan, Ph.D. candidate, Department of Bioinformatics and Biostatistics, University Of Louisville

Title: "Weighted test and test for multiple group comparisons in observational studies"

Although test and test are commonly used for multiple group comparisons in experimental data, these methods can not be directly used to examine group differences in observational studies because of the confounding factors. Since the seminal work by Rosenbaum and Rubin (1983), propensity-score-based inverse probability weighting (IPW) method has become one of the most popular methods for estimating average treatment effect. However, the IPW method has only been applied to compare pairs among multiple treatment groups without controlling the family-wise error rate (FWER). In this article, we propose to examine whether there is an overall significant group difference using a weighted test for a categorical outcome variable and a weighted test for a continuous outcome variable. Only if there is an overall significant group difference, the pairs of interests are further compared. Alternatively, Bonferroni correction is applied to control the FWER for multiple group comparisons. Our extensive simulation studies show that the proposed methods can control the FWER, while the traditional tests have an inflated type I error. To illustrate the practical usage of the proposed tests, we apply the proposed weighted test to investigate whether fruit/vegetable intakes are associated with heart attack using the 2015 Kentucky behavioral risk factor surveillance system dataset, and we apply the weighted test to examine the effect of physical/recreational exercise on weight gain using the national health and nutrition examination survey I epidemiologic follow-up study dataset.

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