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Primary Submission Category: Randomized Studies

Balanced and robust randomized treatment assignments: the finite selection model

Authors: Ambarish Chattopadhyay, Carl Morris, Jose Zubizarreta,

Presenting Author: Ambarish Chattopadhyay*

The Finite Selection Model (FSM) was developed in the 1970s for the design of the RAND Health Insurance Experiment (HIE), one of the largest and most comprehensive social science experiments conducted in the U.S. The idea behind the FSM is that each treatment group takes its turns selecting units in a fair and random order to optimize a common criterion. At each of its turns, a treatment group selects the available unit that maximally improves the combined quality of its resulting group of units in terms of the criterion. In the HIE and beyond, we revisit, formalize, and extend the FSM as a general tool for experimental design.
Leveraging the idea of D-optimality, we propose and analyze a new selection criterion in the FSM. The FSM using the D-optimal selection function has no tuning parameters, is affine invariant, and when appropriate retrieves several classical designs such as randomized block and matched-pair designs. For multi-arm experiments, we propose algorithms to generate a fair and random selection order of treatments. We demonstrate FSM’s performance in a case study based on the HIE and in ten randomized studies from the health and social sciences. We recommend the FSM be considered in experimental design for its conceptual simplicity, balance, and robustness.