Evaluating Innovative Composite Scoring methods to Optimize Power and Sample Size in Clinical Trials

Music City Center 

Clinical trials for complex diseases often use a single primary endpoint, which may overlook the multifaceted nature of complex diseases. As per currently used conventional approaches, if multiple endpoints are assessed in a trial, stringent multiplicity adjustments are required which may inflate sample sizes, trial duration, and costs. A possible solution is to use composite scores. We propose new composite scoring methods with normalized and binary components and compare them with traditional univariate, multivariate, and equally weighted composite scoring approaches. We examine different weighting schemes based on component variability and correlations, identifying scenarios where certain composite scores perform better. Using simulations based on the Assessment of Weekly Administration of Dulaglutide in Diabetes (AWARD) studies, we provide empirical evidence on the best use cases for composite endpoints using type 2 diabetes clinical trials as an example. Our findings offer guidelines for choosing composite score methods that provide power gains and reduce sample size, facilitating better decision-making in trials with multiple outcomes.

composite score

power

sample size

clinical trials

multiplicity 

Biopharmaceutical Section