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