Multivariate Tie-breaker Designs

Abstract

In a tie-breaker design (TBD), subjects with high values of a running variable are given some (usually desirable) treatment, subjects with low values are not, and subjects in the middle are randomized. TBDs are intermediate between regression discontinuity designs (RDDs) and randomized controlled trials (RCTs) by allowing a tradeoff between the resource allocation efficiency of an RDD and the statistical efficiency of an RCT. We study a model where the expected response is one multivariate regression for treated subjects and another for control subjects. For given covariates, we show how to use convex optimization to choose treatment probabilities that optimize a D-optimality criterion. We can incorporate a variety of constraints motivated by economic and ethical considerations. In our model, D-optimality for the treatment effect coincides with D-optimality for the whole regression, and without economic constraints, an RCT is globally optimal. We show that a monotonicity constraint favoring more deserving subjects induces sparsity in the number of distinct treatment probabilities and this is different from preexisting sparsity results for constrained designs. We also study a prospective D-optimality, analogous to Bayesian optimal design, to understand design tradeoffs without reference to a specific data set. We apply the convex optimization solution to a semi-synthetic example involving triage data from the MIMIC-IV-ED database