Preference option randomized design (PORD) for comparative effectiveness research: Statistical power for testing comparative effect, preference effect, selection effect, intent-to-treat effect, and overall effect

Abstract

Comparative effectiveness research trials in real-world settings may require participants to choose between preferred intervention options. A randomized clinical trial with parallel experimental and control arms is straightforward and regarded as a gold standard design, but by design it forces and anticipates the participants to comply with a randomly assigned intervention regardless of their preference. Therefore, the randomized clinical trial may impose impractical limitations when planning comparative effectiveness research trials. To accommodate participants’ preference if they are expressed, and to maintain randomization, we propose an alternative design that allows participants’ preference after randomization, which we call a “preference option randomized design (PORD)”. In contrast to other preference designs, which ask whether or not participants consent to the assigned intervention after randomization, the crucial feature of preference option randomized design is its unique informed consent process before randomization. Specifically, the preference option randomized design consent process informs participants that they can opt out and switch to the other intervention only if after randomization they actively express the desire to do so. Participants who do not independently express explicit alternate preference or assent to the randomly assigned intervention are considered to not have an alternate preference. In sum, preference option randomized design intends to maximize retention, minimize possibility of forced assignment for any participants, and to maintain randomization by allowing participants with no or equal preference to represent random assignments. This design scheme enables to define five effects that are interconnected with each other through common design parameters—comparative, preference, selection, intent-to-treat, and overall/as-treated—to collectively guide decision making between interventions. Statistical power functions for testing all these effects are derived, and simulations verified the validity of the power functions under normal and binomial distributions.