Bayesian Response Adaptive Randomization Research Articles

Assessment of DHA on reducing early preterm birth: the ADORE randomized controlled trial protocol

BackgroundPreterm birth contributes to 0.5 million deliveries in the United States (one of eight pregnancies) and poses a huge burden on public health with costs in the billions. Of particular concern is that the rate of earliest preterm birth (<34 weeks) (ePTB), which has decreased little since 1990 and has the greatest impact on the overall infant mortality, resulting in the greatest cost to society. Docosahexaenoic acid (DHA) supplementation provides a potential high yield, low risk strategy to reduce early preterm delivery in the US by up to 75%. We propose a Phase III Clinical Trial (randomized to low or high dose DHA, double-blinded) to examine the efficacy and safety of high dose DHA supplementation to reduce ePTB. We also plan for a secondary pregnancy efficacy analysis to determine if there is a subset of pregnancies most likely to benefit from DHA supplementation.MethodsBetween 900 and 1200 pregnant women who are ≥ 18 years old and between 12 and 20 weeks gestation will be recruited from three trial experienced academic medical institutions. Participants will be randomly assigned to two daily capsules of algal oil (totaling 800 mg DHA) or soybean and corn oil (0 mg DHA). Both groups will receive a commercially available prenatal supplement containing 200 mg DHA. Therefore, the experimental group will receive 1000 mg DHA/d and the control group 200 mg DHA/d. We will then employ a novel Bayesian response adaptive randomization design that assigns more subjects to the “winning” group and potentially allows for substantially smaller sample size while providing a stronger conclusion regarding the most effective group. The study has an overall Type I error rate of 5% and a power of 90%. Participants are followed throughout pregnancy and delivery for safety and delivery outcomes.DiscussionWe hypothesize that DHA will decrease the frequency of ePTB <34 weeks. Reducing ePTB is clinically important as these earliest preterm deliveries carry the highest risk of neonatal morbidity, as well as contribute significant stress for families and post a large societal burden.Trial registrationThis trial was registered with ClinicalTrials.gov (identifier: NCT02626299) on December 8, 2015. Additional summary details may be found in Table 1.

Statistical inference for response adaptive randomization procedures with adjusted optimal allocation proportions

ABSTRACTSeamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.

A response-adaptive design of initial therapy for emergency department patients with heart failure

Finding safe and effective treatments for acute heart failure syndrome (AHFS) is a high priority. More than 80% of patients with AHFS who present to emergency departments are treated identically with intravenous diuretics, despite recognition of the syndrome's heterogeneity. We hypothesize that matching patient profiles with “personalized” AHFS treatments will improve outcomes. Matching multiple heterogeneous clinical profiles with a number of potentially effective treatments requires an adaptive trial design that can adjust for these complexities. We propose a Bayesian response-adaptive randomization trial design for AHFS patients. Baseline information collected for each patient with AHFS prior to randomization includes blood pressure, renal function, and dyspnea severity. The primary outcome is discharge readiness within 23h of presentation and no unplanned emergency visits or admissions for acute heart failure within 7days of discharge. We use a Bayesian logistic regression model to characterize the association between primary outcome and patient profile. We adaptively randomize patients to one of five treatments, basing the randomization probability on the cumulative data from the ongoing trial and fitting results from the regression model. Simulations show high probability of selecting the best treatment corresponding to the patient's profile while allocating more patients to the efficacious treatments within the trial.

Hyperbaric oxygen brain injury treatment (HOBIT) trial: a multifactor design with response adaptive randomization and longitudinal modeling.

The goals of phase II clinical trials are to gain important information about the performance of novel treatments and decide whether to conduct a larger phase III trial. This can be complicated in cases when the phase II trial objective is to identify a novel treatment having several factors. Such multifactor treatment scenarios can be explored using fixed sample size trials. However, the alternative design could be response adaptive randomization with interim analyses and additionally, longitudinal modeling whereby more data could be used in the estimation process. This combined approach allows a quicker and more responsive adaptation to early estimates of later endpoints. Such alternative clinical trial designs are potentially more powerful, faster, and smaller than fixed randomized designs. Such designs are particularly challenging, however, because phase II trials tend to be smaller than subsequent confirmatory phase III trials. The phase II trial may need to explore a large number of treatment variations to ensure that the efficacy of optimal clinical conditions is not overlooked. Adaptive trial designs need to be carefully evaluated to understand how they will perform and to take full advantage of their potential benefits. This manuscript discusses a Bayesian response adaptive randomization design with a longitudinal model that uses a multifactor approach for predicting phase III study success via the phase II data. The approach is based on an actual clinical trial design for the hyperbaric oxygen brain injury treatment trial. Specific details of the thought process and the models informing the trial design are provided. Copyright © 2016 John Wiley & Sons, Ltd.

A Bayesian Response-Adaptive Randomization Design for Clinical Trials with Survival Endpoints

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. responseadaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency, which also have the advantage of assigning fewer patients to inferior treatment arms. The RA design based on binary outcome is commonly used in clinical trial where “success” is defined as the desired (or undesired) event occurring within (or beyond) a clinical relevant time. As patients entering into trial sequentially, only part of patients have sufficient follow-up during interim analysis. This results in a loss of information as it is unclear how patients without sufficient follow-up should be handled. Alternatively, adaptive design for survival trial was proposed for this type of trial. However, most of current practice assumes the event times following a pre-specified parametric distribution. We adopt a nonparametric model of survival outcome which is robust to model of event time distribution, and then apply it to response-adaptive design. The operating characteristics of the proposed design along with parametric design are compared by simulation studies, including their robustness properties with respect to model misspecifications.

Bayesian response adaptive randomization using longitudinal outcomes.

The response adaptive randomization (RAR) method is used to increase the number of patients assigned to more efficacious treatment arms in clinical trials. In many trials evaluating longitudinal patient outcomes, RAR methods based only on the final measurement may not benefit significantly from RAR because of its delayed initiation. We propose a Bayesian RAR method to improve RAR performance by accounting for longitudinal patient outcomes (longitudinal RAR). We use a Bayesian linear mixed effects model to analyze longitudinal continuous patient outcomes for calculating a patient allocation probability. In addition, we aim to mitigate the loss of statistical power because of large patient allocation imbalances by embedding adjusters into the patient allocation probability calculation. Using extensive simulation we compared the operating characteristics of our proposed longitudinal RAR method with those of the RAR method based only on the final measurement and with an equal randomization method. Simulation results showed that our proposed longitudinal RAR method assigned more patients to the presumably superior treatment arm compared with the other two methods. In addition, the embedded adjuster effectively worked to prevent extreme patient allocation imbalances. However, our proposed method may not function adequately when the treatment effect difference is moderate or less, and still needs to be modified to deal with unexpectedly large departures from the presumed longitudinal data model.