Outcome-adaptive Randomization Research Articles

Outcome-Adaptive Randomization: Is It Useful?

Outcome-adaptive randomization is one of the possible elements of an adaptive trial design in which the ratio of patients randomly assigned to the experimental treatment arm versus the control treatment arm changes from 1:1 over time to randomly assigning a higher proportion of patients to the arm that is doing better. Outcome-adaptive randomization has intuitive appeal in that, on average, a higher proportion of patients will be treated on the better treatment arm (if there is one). In both the randomized phase II and phase III settings with a short-term binary outcome, we compare outcome-adaptive randomization with designs that use 1:1 and 2:1 fixed-ratio randomizations (in the latter, twice as many patients are randomly assigned to the experimental treatment arm). The comparisons are done in terms of required sample sizes, the numbers and proportions of patients having an inferior outcome, and we restrict attention to the situation in which one treatment arm is a control treatment (rather than the less common situation of two experimental treatments without a control treatment). With no differential patient accrual rates because of the trial design, we find no benefits to outcome-adaptive randomization over 1:1 randomization, and we recommend the latter. If it is thought that the patient accrual rates will be substantially higher because of the possibility of a higher proportion of patients being randomly assigned to the experimental treatment (because the trial will be more attractive to patients and clinicians), we recommend using a fixed 2:1 randomization instead of an outcome-adaptive randomization.

Adaptive Randomization for Multiarm Comparative Clinical Trials Based on Joint Efficacy/Toxicity Outcomes

We present an outcome-adaptive randomization (AR) scheme for comparative clinical trials in which the primary endpoint is a joint efficacy/toxicity outcome. Under the proposed scheme, the randomization probabilities are unbalanced adaptively in favor of treatments with superior joint outcomes characterized by higher efficacy and lower toxicity. This type of scheme is advantageous from the patients' perspective because on average, more patients are randomized to superior treatments. We extend the approximate Bayesian time-to-event model in Cheung and Thall (2002, Biometrics 58, 89-97) to model the joint efficacy/toxicity outcomes and perform posterior computation based on a latent variable approach. Consequently, this allows us to incorporate essential information about patients with incomplete follow-up. Based on the computed posterior probabilities, we propose an AR scheme that favors the treatments with larger joint probabilities of efficacy and no toxicity. We illustrate our methodology with a leukemia trial that compares three treatments in terms of their 52-week molecular remission rates and 52-week toxicity rates.

Using short‐term response information to facilitate adaptive randomization for survival clinical trials

Increased survival is a common goal of cancer clinical trials. Owing to the long periods of observation and follow-up to assess patient survival outcome, it is difficult to use outcome-adaptive randomization in these trials. In practice, often information about a short-term response is quickly available during or shortly after treatment, and this short-term response is a good predictor for long-term survival. For example, complete remission of leukemia can be achieved and measured after a few cycles of treatment. It is a short-term response that is desirable for prolonging survival. We propose a new design for survival trials when such short-term response information is available. We use the short-term information to 'speed up' the adaptation of the randomization procedure. We establish a connection between the short-term response and the long-term survival through a Bayesian model, first by using prior clinical information, and then by dynamically updating the model according to information accumulated in the ongoing trial. Interim monitoring and final decision making are based upon inference on the primary outcome of survival. The new design uses fewer patients, and can more effectively assign patients to the better treatment arms. We demonstrate these properties through simulation studies.

Continuous Bayesian adaptive randomization based on event times with covariates

In comparative clinical trials, the randomization probabilities may be unbalanced adaptively by utilizing the interim data available at each patient's entry time to favour the treatment or treatments having comparatively superior outcomes. This is ethically appealing because, on average, more patients are assigned to the more successful treatments. Consequently, physicians are more likely to enroll patients onto trials where the randomization is outcome-adaptive rather than balanced in the conventional manner. Outcome-adaptive methods based on a binary variable may be applied by reducing an event time to the indicator of the event's occurrence within a predetermined time interval. This results in a loss of information, however, since it ignores the censoring times of patients who have not experienced the event but whose evaluation interval is not complete. This paper proposes and compares exact and approximate Bayesian outcome-adaptive randomization procedures based on time-to-event outcomes. The procedures account for baseline prognostic covariates, and they may be applied continuously over the course of the trial. We illustrate these methods by application to a phase II selection trial in acute leukaemia. A simulation study in the context of this trial is presented.

Evaluation of bias for outcome adaptive randomization designs with binary endpoints

Evaluation of bias for outcome adaptive randomization designs with binary endpoints