Time trends with response-adaptive randomization: The inevitability of inefficiency

Response-adaptive randomization, which changes the randomization ratio as a randomized clinical trial progresses, is inefficient as compared to a fixed 1:1 randomization ratio in terms of increased required sample size. It is also known that response-adaptive randomization leads to biased treatment effects if there are time trends in the accruing outcome data, for example, due to changes in the patient population being accrued, evaluation methods, or concomitant treatments. Response-adaptive-randomization analysis methods that account for potential time trends, such as time-block stratification or re-randomization, can eliminate this bias. However, as shown in this Commentary, these analysis methods cause a large additional inefficiency of response-adaptive randomization, regardless of whether a time trend actually exists.

The Integrative Approach to Assessing and Managing Risk

This chapter articulates the integrative approach to assessing and managing risk in research. This framework is grounded, not in role-related obligations, but in respect for the basic interests of persons. It models uncertainty as a property of a moderately idealized community of diverse experts, and it shows how studies that are designed to reduce conflict or uncertainty within such a community can reconcile the production of socially valuable information with respect for the status of research participants as free and equal. The merits of this approach relative to prominent alternatives, including component analysis, clinical equipoise, the non-exploitation view and the net risk view are elaborated at length. The merits off the integrative approach are demonstrated by showing how this framework allows trial that use response adaptive randomization to be designed in ways that respect a principle of equal concern and a series of related ethical requirements.

CTNI-40. EVALUATING FEASIBILITY AND EFFICIENCY OF PHASE II ADAPTIVE PLATFORM TRIAL DESIGNS BASED ON THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGhT) EXPERIENCE

BACKGROUND The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial with Bayesian adaptive randomization and deep genomic profiling to more efficiently test experimental agents in newly diagnosed glioblastoma and to prioritize therapies for late-stage testing. METHODS In the ongoing INSIGhT trial, patients with newly diagnosed MGMT-unmethylated glioblastoma are randomized to the control arm or one of three experimental therapy arms (CC-115, abemaciclib, and neratinib). The control arm therapy is radiotherapy with concomitant and adjuvant temozolomide, and primary endpoint is overall survival. Randomization has been adapted based on Bayesian estimation of biomarker-specific probability of treatment impact on progression-free survival (PFS). All tumors undergo detailed molecular sequencing, and this is facilitated with the companion ALLELE protocol. To evaluate feasibility of this approach, we assessed the status of this ongoing trial. RESULTS Since INSIGhT was activated 4.3 years ago, it has expanded to include 12 sites across the United States. A total of 247 patients have been enrolled. Randomization probabilities have been repeatedly adjusted over time based upon early PFS results to alter the randomization ratio from standard 1:1:1:1 randomization. All three arms have completed accrual and efficacy estimates are available based upon comparison to the common control arm in context of relevant biomarkers. There are 87 patients alive and in follow-up, and there are ongoing plans to add additional arms to evaluate further treatments in the future. CONCLUSION The INSIGhT trial demonstrates that a multi-center Bayesian adaptive platform trial is a feasible and effective approach to help prioritize therapies and biomarkers for newly diagnosed GBM. The trial has maintained robust accrual, and the simultaneous testing of multiple agents, sharing a common control arm and adaptive randomization serve as features to increase trial efficiency relative to traditional clinical trial designs.

CTNI-05. PRELIMINARY RESULTS OF THE NERATINIB ARM IN THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGHT): A PHASE II PLATFORM TRIAL USING BAYESIAN ADAPTIVE RANDOMIZATION

BACKGROUND EGFR is amplified in over 50% of glioblastoma and 20-30% have EGFRvIII mutations. Neratinib is a potent inhibitor of EGFR/HER2 approved for metastatic HER2+ breast cancer. To efficiently evaluate the potential impact of neratinib on overall survival (OS) in newly-diagnosed glioblastoma and to simultaneously develop information regarding potential genomic biomarker associations, neratinib was included as an arm on the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. INSIGhT is a phase II platform trial using response adaptive randomization and deep genomic profiling to more efficiently test experimental agents in MGMT unmethylated glioblastoma and accelerate identification of novel therapies for phase III testing. Initial randomization was equal between neratinib, control, and two other experimental arms but subsequent randomization was adapted based on efficacy as determined by progression-free survival (PFS). We report preliminary results for the neratinib arm. METHODS Patients with newly diagnosed MGMT-unmethylated glioblastoma were randomized to receive either radiotherapy with concomitant and adjuvant temozolomide or standard radiochemotherapy followed by adjuvant neratinib (240 mg daily). Treatment continued until progression or development of unacceptable toxicities. The primary endpoint was OS. Association between neratinib efficacy and EGFR amplification was also investigated. RESULTS There were 144 patients (70 control; 74 neratinib). Neratinib was reasonably well-tolerated with no new toxicity signals identified. PFS was compared (HR 0.84; p=0.38, logrank test – not significant) between the neratinib (median 6.05 months) and control (median 5.82 months) arms. For patients EGFR pathway activation the PFS HR was 0.53 (p-value=0.03 – significant, median PFS: neratinib, 6.21 months, control, 5.26 months). However, there was no significant improvement in OS in EGFR amplified/mutated patients (HR 1.05; p-value 0.87) between neratinib (median 14.2) compared to the control arm (median 14.6). CONCLUSION Neratinib prolonged PFS in the EGFR positive subpopulation but there was no overall PFS benefit, or any OS improvement.

Should RECOVERY have used Response Adaptive Randomization? Evidence from a simulation study

Background The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or `fixed’ randomization (FR) to allocate patients to treatments. We assessed the impact of implementing response adaptive randomization (RAR) within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP trial.Methods Trial data was simulated to closely match the data for patients allocated to either standard care or dexamethasone in the RECOVERY trial from March-June 2020, representing two out of five arms tested throughout this period. Two forms of FR and two forms of RAR were tested. Randomization strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients’ respiratory support level.ResultsRAR strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific RAR reduced mortality rates even further. RAR did not induce any meaningful bias in treatment effect estimates, but reduced statistical power compared to FR, with subgroup level adaptive randomizations exhibiting the largest power reduction.ConclusionsUsing RAR within RECOVERY could have resulted in fewer deaths in the trial. However, a larger trial would have been needed to attain the same study power. This could potentially have prolonged the time to full approval of the drug, unless RAR itself led to an increased recruitment rate. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question of our time. RAR deserves to be considered as a design feature in future trials of COVID-19 and other diseases.

Estimating Conditional Power for Sequential Monitoring of Covariate Adaptive Randomized Designs: The Fractional Brownian Motion Approach

Conditional power based on classical Brownian motion (BM) has been widely used in sequential monitoring of clinical trials, including those with the covariate adaptive randomization design (CAR). Due to some uncontrollable factors, the sequential test statistics under CAR procedures may not satisfy the independent increment property of BM. We confirm the invalidation of BM when the error terms in the linear model with CAR design are not independent and identically distributed. To incorporate the possible correlation structure of the increment of the test statistic, we utilize the fractional Brownian motion (FBM). We conducted a comparative study of the conditional power under BM and FBM. It was found that the conditional power under FBM assumption was mostly higher than that under BM assumption when the Hurst exponent was greater than 0.5.