Preference-adaptive randomization in comparative effectiveness studies

Abstract

Background: Determination of comparative effectiveness in a randomized controlled trial requires consideration of an intervention's comparative uptake (or acceptance) among randomized participants and the intervention's comparative efficacy among participants who use their assigned intervention. If acceptance differs across interventions, then simple randomization of participants can result in post-randomization losses that introduce bias and limit statistical power.

Methods: We develop a novel preference-adaptive randomization procedure in which the allocation probabilities are updated based on the inverse of the relative acceptance rates among randomized participants in each arm. In simulation studies, we determine the optimal frequency with which to update the allocation probabilities based on the number of participants randomized. We illustrate the development and application of preference-adaptive randomization using a randomized controlled trial comparing the effectiveness of different financial incentive structures on prolonged smoking cessation.

Results: Simulation studies indicated that preference-adaptive randomization performed best with frequent updating, accommodated differences in acceptance across arms, and performed well even if the initial values for the allocation probabilities were not equal to their true values. Updating the allocation probabilities after randomizing each participant minimized imbalances in the number of accepting participants across arms over time. In the smoking cessation trial, unexpectedly large differences in acceptance among arms required us to limit the allocation of participants to less acceptable interventions. Nonetheless, the procedure achieved equal numbers of accepting participants in the more acceptable arms, and balanced the characteristics of participants across assigned interventions.

Conclusions: Preference-adaptive randomization, coupled with analysis methods based on instrumental variables, can enhance the validity and generalizability of comparative effectiveness studies. In particular, preference-adaptive randomization augments statistical power by maintaining balanced sample sizes in efficacy analyses, while retaining the ability of randomization to balance covariates across arms in effectiveness analyses.

Trial registration: ClinicalTrials.gov, NCT01526265; 31 January 2012.

Figures

Figure 1

Probability of being allocated to each incentive structure in each of the four strata over the enrollment period. A, B, C, and D indicate protocol changes in the randomization procedure. These changes were made to address observed disparities in acceptance rates. On 11 April 2012, we implemented a 60%/40% split between [usual care + individual reward + collaborative reward arms] and [individual deposit + competitive deposit arms] (A). On 25 April 2012, we changed to an 80%/20% split between the same two groups (B). On 14 September 2012, we changed to a 50%/50% split between the same two groups (C). On 26 September 2012, we retained this 50%/50% split, continued the adaptive randomization for the deposit arms, but among the 50% of participants randomized to the group containing the other three arms, we fixed the allocation probabilities for the usual care (15%), individual reward (15%) and collaborative reward (70%) arms to bolster assignment to the latter (D).