UCLA

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 endpoints were targeted. The traditional hypothesis testing paradigm that tests significance on one primary outcome did not suffice. Past multiple endpoint 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 adjust for correlation, providing updated critical values and p values. Our method is implemented through an R package and applied to the Philani data to test the intervention's overall effect.