Work

Applying permutation tests with adjustment for covariates and attrition weights to randomized trials of health‐services interventions

Year: 2008

Type: article

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.

Source: Statistics in Medicine

DOI: 10.1002/sim.3453

Authors: Lingqi Tang, Naihua Duan, Ruth Klap, Joan Rosenbaum Asarnow, Thomas R. Belin

Institutions: University of California, Los Angeles, New York State Psychiatric Institute, Columbia University

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, Agency for Healthcare Research and Quality, National Institute of Mental Health