Dr. Shenzhen Zu, PhD, Research Statistician, Abbott Lab will be speaking about:

Propensity Score Matching in Randomized Clinical Trials with Two or More Arms

Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health-care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, inpractice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. In this talk, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. We evaluate the properties of these extended designs in simulation studies and illustrate these methods in proposing a design for a cardiovascular trial.