Multiple‐objective response‐adaptive repeated measurement designs in clinical trials for binary responses

Abstract

A multiple-objective allocation strategy was recently proposed for constructing response-adaptive repeated measurement designs for continuous responses. We extend the allocation strategy to constructing response-adaptive repeated measurement designs for binary responses. The approach with binary responses is quite different from the continuous case, as the information matrix is a function of responses, and it involves nonlinear modeling. To deal with these problems, we first build the design on the basis of success probabilities. Then we illustrate how various models can accommodate carryover effects on the basis of logits of response profiles as well as any correlation structure. Through computer simulations, we find that the allocation strategy developed for continuous responses also works well for binary responses. As expected, design efficiency in terms of mean squared error drops sharply, as more emphasis is placed on increasing treatment benefit than estimation precision. However, we find that it can successfully allocate more patients to better treatment sequences without sacrificing much estimation precision.

Response adaptive intervention allocation in stepped‐wedge cluster randomized trials

“…The above formulation has been used previously in RA design, for example, for sequence specification in individually randomized crossover trials. 16 Nonetheless, specifying b (·) is complex for SW-CRTs because allocation is to be adapted for clusters already in the trial. In practice, there may be good reason to make b (·) a complex function that, for example, incorporates penalties for the speed or cost of the intervention roll-out if its availability is limited.…”

“…The above formulation has been used previously in RA design, for example, for sequence specification in individually randomized crossover trials 16 . Nonetheless, specifying is complex for SW‐CRTs because allocation is to be adapted for clusters already in the trial.…”

Practical and Optimal Crossover Designs for Clinical Trials

“…Liang et al [12] and Li [13] extended their multiple objective function to binary responses and derived the information matrix for estimated success probabilities for binary responses. The observed number of successes for each treatment sequence was used for the evaluation function f .…”

“…However, these studies utilized the acquired information using only a single objective. Many authors proposed a multiple objective adaptive design for continuous responses where they defined an objective function with two components, controlled by a weight parameter [11][12][13].…”