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Activity Number: 351
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Mental Health Statistics Section
Abstract #313135
Title: A Test of Intervention Effect in a Study with Multiple Correlated Outcomes: Counting Significant Treatment-Control Differences
Author(s): Jessica Harwood*+ and Robert E. Weiss and Warren Comulada
Companies: and University of California, Los Angeles and University of California, Los Angeles Center for Community Health
Keywords: Monte Carlo ; combining p-values ; multiplicity ; correlated outcomes ; sign test ; randomized controlled trial
Abstract:

Behavioral interventions are increasingly based on holistic approaches to health with an understanding that health-related behaviors are linked. A motivating example is provided by the Philani study, an intervention trial conducted to improve the health of South African mothers and their children. Inter-related health problems around maternal alcohol use, malnutrition, and HIV were addressed; multiple end points were targeted. The traditional hypothesis testing paradigm that tests significance on a primary outcome did not suffice. Past multiple end-point studies have utilized a sign test on the number of estimated differences between treatment and control that favor the intervention. However, in order to preserve Type 1 error, one must account for correlations among the outcomes. We propose an alternative approach that counts the number of significant treatment-control differences. Monte Carlo simulation is used to assign a p-value by evaluating the distribution of the sum of correlated binary variables for the presence of significant results. Our method is implemented through an R package and applied to the Philani data to test the intervention's overall effect.


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