04 Causal Inference and Counterfactual Random Effects for Trials with Repeatedly Measured Intermediate and Primary Endpoints

Oregon Convention Center 

Valid intermediate endpoints may serve as surrogate markers for a clinical outcome to conduct a randomized trial most efficiently. This work aims to develop causal inference methods that can determine whether or not repeated measures of biomarkers throughout a type one diabetes trial could be used in place of the current primary endpoints, which are also collected longitudinally. The proposed methods use observed and counterfactual outcomes to capture the trajectories of individuals and evaluate such endpoints. This framework uses potential outcomes and the principal stratification framework via mixed models. Ultimately, this allows us to assess the validity of the endpoint by calculating a causal effect predictiveness curve from the distribution of random effects for both the surrogate and clinical endpoints.