Strategies to evaluate treatment effects in clinical trials for emerging infectious diseases

Abstract

Background

Swift regulatory approval of therapeutic interventions is crucial during emerging infectious disease outbreaks. However, variability in treatment effects based on disease severity or subgroups complicates trial design and endpoint selection. Prioritized composite endpoints can capture treatment effects across diverse clinical courses; however, their performance under heterogeneous treatment effects remains uncertain. This study uses simulation to evaluate trial design strategies in such contexts.

Methods

This study examines eight combinations of population and endpoint strategies to optimize trial design in emerging infectious diseases: evaluating treatment in the overall population and subgroups, with various endpoint choices including single, multiple, and prioritized composite endpoints. Simulated data was generated using multistate models based on the ACTT-1 study. Eight treatment effect scenarios, some exhibiting heterogeneity, were considered to evaluate ability to demonstrate efficacy.

Results

In scenarios without heterogeneous treatment effects, analyses in the overall population generally showed higher power than subgroup analyses. Time to recovery had relatively high power, while prioritized composite and multiple endpoints were comparable. In scenarios with treatment effect heterogeneity by baseline disease severity, power was higher in effective subgroups than in the overall population. Prioritized composite endpoints showed high power in scenarios where the treatment was effective on distinct endpoints in each subgroup.

Conclusions

For drug development in emerging infectious diseases with limited information, it is preferable to focus on evaluating prioritized composite or multiple endpoints in the overall population. Stratified analysis can be more powerful than unstratified analysis and should be considered for the primary analysis in the overall population.