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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Applying permutation tests with adjustment for covariates and attrition weights to randomized trials of
Lingqi Tang1, Naihua Duan, Ruth Klap
1Health Services Research Center, Semel Institute for Neuroscience and Human Behavior, UCLA, 10920 Wilshire Boulevard, Suite 300, Los Angeles, CA 90024, USA. lqtang@ucla.edu
Abstract:
Using a health-services study as an illustrative example of longitudinal randomized field research with the potential for participants to be lost to follow-up, we apply a permutation test where the treatment indicator variable is randomly permuted in the context of regression models with covariates and attrition weighting. The test is applied to a multi-site randomized intervention trial of a quality-improvement program for adolescent depression treatment in primary-care settings, in which regression models were used to assess intervention effects with weights used to adjust for attrition bias. The foundation and motivation for this approach to the analysis are considered with attention to the demands associated with implementing such a strategy. The results from the permutation tests were qualitatively similar to the results obtained from conventional parametric models, and in fact suggested that the significance level from the conventional t-test was understated in this application.
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