Screening Experiments for Developing Dynamic Treatment Regimes

Abstract

Dynamic treatment regimes are time-varying treatments that individualize sequences of treatments to the patient. The construction of dynamic treatment regimes is challenging because a patient will be eligible for some treatment components only if he has not responded (or has responded) to other treatment components. In addition, there are usually a number of potentially useful treatment components and combinations thereof. In this article, we propose new methodology for identifying promising components and screening out negligible ones. First, we define causal factorial effects for treatment components that may be applied sequentially to a patient. Second, we propose experimental designs that can be used to study the treatment components. Surprisingly, modifications can be made to (fractional) factorial designs—more commonly found in the engineering statistics literature—for screening in this setting. Furthermore, we provide an analysis model that can be used to screen the factorial effects. We demonstrate the proposed methodology using examples motivated in the literature and also via a simulation study.