Abstract: Win measures, such as win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR), have become popular approaches for the analysis of hierarchical endpoints in clinical studies. For trial design with these win measures based on hierarchical endpoints, sample size and power calculations are often based on simulation studies that can be cumbersome.  Existing sample size and power formulas require investigators to specify clinically significant and meaningful magnitude of win measures and probability of ties that are difficult to elicit based on prior published literature or preliminary data.  To overcome these difficulties, we express the win measures as function of marginal win measures that are readily available under independence assumption and this provides a novel way to specify a meaningful and justifiable magnitude of win measures and the magnitude of probability of ties. We built a very flexible R shiny app (https://duke-som.shinyapps.io/Hierarchical_Endpoints/) that can quickly compute a sample size with a given power or compute a power with a given sample size for any clinical trial design with two groups for any number of hierarchical endpoints, any type of endpoints (time to event, counts, binary, or ordinal), and any order of hierarchy of these endpoints.
Speaker: Huiman Barnhart and Dylan Thibault
Affiliation: Duke
Title: Sample Size or Power Calculations for Trial Design with Hierarchical Endpoints - an R Shiny App
Abstract_Sanitzed: Win measures, such as win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR), have become popular approaches for the analysis of hierarchical endpoints in clinical studies. For trial design with these win measures based on hierarchical endpoints, sample size and power calculations are often based on simulation studies that can be cumbersome.  Existing sample size and power formulas require investigators to specify clinically significant and meaningful magnitude of win measures and probability of ties that are difficult to elicit based on prior published literature or preliminary data.  To overcome these difficulties, we express the win measures as function of marginal win measures that are readily available under independence assumption and this provides a novel way to specify a meaningful and justifiable magnitude of win measures and the magnitude of probability of ties. We built a very flexible R shiny app (https//duke-som.shinyapps.io/Hierarchical_Endpoints/) that can quickly compute a sample size with a given power or compute a power with a given sample size for any clinical trial design with two groups for any number of hierarchical endpoints, any type of endpoints (time to event, counts, binary, or ordinal), and any order of hierarchy of these endpoints.