Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials

Background Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. Methods Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. Results The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. Conclusions We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

Treatment of MDS, AML and CMML Relapse after Allogeneic Blood Stem Cell Transplantation with Azacitidine, Lenalidomide and Donor Lymphocyte Infusions - Final Results of the Prospective Azalena-Trial (NCT02472691)

Background Azacitidine (Aza) in combination with donor lymphocyte infusions (DLI) is an established treatment option for pts with relapse of myeloid malignancies after allo-SCT. Accounting for its immunomodulatory and anti-leukemic properties, we considered Lenalidomide (Len) to be a synergistic partner for Aza and DLI that may further improve response rate and outcome. To investigate the tolerability and efficacy of the combination of Aza, Len and DLI as first salvage therapy for relapsed MDS, AML and CMML after allo-SCT we performed a prospective, multicenter, single-arm phase-II trial. Results from two safety interim analyses have previously been reported. Here, we report the final results from this investigator-initiated trial. Design/Methods: Patients with relapse of MDS, AML and CMML after first allo-SCT were eligible. Envisaged treatment according to the protocol consisted of up to 8 cycles Aza (75 mg/m 2/d d1-7, every 28 days) and up to 3 DLI with increasing T cell dosages (0.5×10 6 - 1.5×10 7 cells/kg). Len was administered concomitantly for 21 days of a 28-day cycle. Following a positive first interim safety analysis in 10 patients the daily dose of Len was increased from 2.5 to 5mg. The primary endpoint of the study was safety, while secondary efficacy endpoints included response type and rates, time to and duration of response and overall survival. Results: Overall, 50 pts with molecular (n=29, 58%) or hematological (n=21, 42%) relapse of MDS (n=24, 48%), AML (n=23, 46%) or CMML (n=3, 6%) detected after median of 233 days (range, 98 to 2659) after allo-SCT were included. Fourteen patients (28%) received Len at a daily dosage of 2.5 mg and 36 patients (72%) at a daily dosage of 5 mg with no DLTs observed in the interim analyses. Median number of Len cycles per patient was 7 (range, 1 to 8) with no differences between the two dose levels. Concomitantly, 34 pts (68%) received at least one DLI (median: 3, range: 1-11). Overall response rate (ORR) during treatment was 56% (CR n=25, 50%, PR n=3, 6%). ORR and CR rates did not differ between Len dose levels. Of interest, CR rate did not differ between pts treated at the stage of molecular relapse and those initiated at hematological relapse (52% vs. 48%). Median time to CR was 112 days (range 1-286) corresponding to 4 cycles (range 1 to 8). At the time of data lock, 20 patients (80%) were still in CR without additional therapy for a median of 15 months, while 5 patients (20%) had relapsed again after a median of 8 months. With a median follow-up of 20 months median OS was 21 months and 1-year OS rate 65%. While therapy-related CTC grade III/IV neutropenia (92%), thrombopenia (80%) or anemia (36%) occurred frequently, drug-related non-hematological adverse events (AE) >grade II were rare and mainly consisted of gastrointestinal toxicity (6%), laboratory findings (28%) and infections (22%). Twenty-three pts (46%) developed acute GvHD including 5 patients (10%) with grade III/IV aGvHD, and 26 pts (52%) chronic GvHD (mild n=10; moderate n=11; severe n=5). During the study period, 3 secondary malignancies (squamous cell, basal cell and vulvar carcinoma) occurred. There were no therapy related deaths. Conclusion: Len up to a dosage to 5 mg/day can be safely added to the combination of AZA and DLI without excess of GvHD and toxicity. Furthermore, these data suggest that the combination of Aza, Len and DLI has promising clinical activity for relapse of myeloid malignancies after allo-SCT and is able to induce durable responses and survival in a substantial proportion of pts. Disclosures Schroeder: Celgene: Honoraria, Other: Travel support, Research Funding. Stelljes: Kite/Gilead: Consultancy, Speakers Bureau; MSD: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Celgene/BMS: Consultancy, Speakers Bureau; Medac: Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau. Holtick: Celgene: Honoraria; Sanofi: Honoraria. Germing: Janssen: Honoraria; Bristol-Myers Squibb: Honoraria, Other: advisory activity, Research Funding; Jazz Pharmaceuticals: Honoraria; Celgene: Honoraria; Novartis: Honoraria, Research Funding. Kröger: AOP Pharma: Honoraria; Celgene: Honoraria, Research Funding; Gilead/Kite: Honoraria; Jazz: Honoraria, Research Funding; Neovii: Honoraria, Research Funding; Novartis: Honoraria; Riemser: Honoraria, Research Funding; Sanofi: Honoraria. Kobbe: Celgene: Research Funding. OffLabel Disclosure: Lenalidomide is not licensed for AML, CMML and advanced MDS except for MDS with isolated del5q

Interim Analyses of Overall Survival (OS) from the TOURMALINE MM3 & MM4 Studies of Ixazomib Maintenance Following Primary Therapy in Multiple Myeloma (MM)

Background: Two global, randomized, placebo (pbo)-controlled phase 3 studies of single-agent ixazomib (ixa) maintenance therapy are currently ongoing for newly diagnosed MM patients following primary therapy that included autologous stem cell transplantation (ASCT) (MM3, NCT02181413) or excluded ASCT (MM4, NCT02312258). Both trials have demonstrated statistically significant and clinically meaningful improvement in their primary endpoint of progression-free survival (PFS): for MM3, median 26.5 months (mos) ixa vs 21.3 mos pbo (hazard ratio [HR] 0.720, 95% confidence interval [CI] 0.582-0.890, P=0.002); for MM4, median 17.4 mos for ixa vs 9.4 mos for pbo (HR 0.659, 95% CI 0.542-0.801, P<0.001). Data for the key secondary endpoint, OS, have not previously been published. Methods: Full methods have previously been reported (Dimopoulos, Lancet 2019; Dimopoulos, J Clin Oncol 2020). Eligible patients (MM3, N=656; MM4, N=706) were randomized 3:2 to receive maintenance therapy with single agent ixa or pbo for a maximum of approximately 24 mos (26 cycles, to the nearest complete cycle) or until progressive disease or unacceptable toxicity, whichever occurred first. Results: At the most recent data cut-off (MM3, 29 January 2021; MM4, 15 October 2020), with median follow up of 64 mos and 36 mos, respectively, 27% (MM3, 174/656) and 29% (MM4, 203/706) of the intention-to-treat (ITT) population had OS events. In MM3, the 5-year Kaplan-Meier estimate for OS was 74% for ixa and 73% for pbo, though the median OS had not yet been reached in either arm (HR 1.008, 95% CI 0.744-1.367, P=0.958; Figure). In MM4, the 5-year Kaplan-Meier OS estimate was 55% for ixa and 56% for pbo, though the median OS had also not yet been reached in either arm (HR 1.136, 95% CI 0.853-1.514, P=0.382; Figure). No new safety signals were identified, and the incidence of new primary malignancies in both studies was similar between ixa and pbo. Conclusions: These most current OS data for MM3 and MM4 have not demonstrated a statistically significant difference for the ixa or the pbo arm to date. After 64 mos of follow-up in MM3, the risk of OS does not differ between the study arms. After 36 mos of follow-up in MM4, the OS HR shows a trend that favors the pbo arm. Because interim analyses of OS may be overrepresented by deaths in patients who did not benefit from maintenance therapy, it is not known to what extent these results will be reflective of the ITT population at the time of the final analyses. As treatment options, including anti-CD38 mAb and other new mechanisms of action, for salvage therapies following progression continue to expand, OS is increasingly being confounded by subsequent therapies. Hence, demonstrating OS advantage for early line MM therapies is becoming increasingly challenging. OS data continue to be collected in these studies for later analyses. Figure 1 Figure 1. Disclosures Dimopoulos: Takeda: Honoraria; Amgen: Honoraria; Beigene: Honoraria; Janssen: Honoraria; BMS: Honoraria. Lonial: AMGEN: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Merck: Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria. Chng: Johnson and Johnson: Honoraria, Research Funding; BMS/Celgene: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Pfizer: Honoraria. Iida: Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Ono: Honoraria, Research Funding; Chugai: Research Funding; Celgene: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Daiichi Sankyo: Research Funding; Glaxo SmithKlein: Research Funding; Amgen: Research Funding; Abbvie: Research Funding; Bristol-Myers Squibb: Research Funding. Mateos: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bluebird bio: Honoraria; Sea-Gen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria; Oncopeptides: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Suryanarayan: Takeda: Current Employment. Vorog: Takeda: Current Employment. Fergus: Takeda: Current Employment. Labotka: Takeda: Current Employment. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib as maintenance treatment for multiple myeloma following stem cell transplantation or induction therapy in newly diagnosed patients.

An Audit of Interim Analyses of Randomized Controlled Trials [RCTs] Published in Three High Impact Factor Medical Journals over a seven-year period [2012-2018]

Background: An interim analysis is an integral component of clinical research and drug development in particular and helps reduce ‘time to market’ for intervention or stop further development of unsafe and ineffective interventions. In this audit, we evaluated the extent of the use of interim analyses in published RCTs in three leading journals and their impact on regulatory approval. Methodology: RCTs published in JAMA, NEJM, and Lancet in the year 2012 to 2018 were extracted. Each RCT was scrutinized using the filter term ‘Interim’. Both descriptive and inferential statistics were used to analyse the data. The factors [therapeutic areas, nature of interventions, source of funding, and phases of trials] associated with Interim analysis and its impact on drug approval were analysed. Results: The majority of RCTs with interim analysis belonged to oncology [27%] and cardiology [17.2%] and were related to drugs [70%]. The majority of the RCTs were in phase 3 [56.3%] and funded exclusively by the Pharmaceutical industry [36.2%]. A total of 2% and 14% of studies lead to accelerated approval and normal regulatory approval. The choice of alpha spending function was not mentioned in 44.8% of studies, and 21% of studies used the O-Brien Fleming method. A total of 18.5% of studies were stopped early. The oncology trials, drug as intervention, and Phase 3 trials were associated with the conduct of interim analysis, which was associated with significantly higher numbers of accelerated and routine regulatory approvals. Conclusion: The majority of the RCTs with interim analysis were from oncology, and most did not report a stopping rule. Interventions that were drugs [rather than devices or surgical procedures] and phase 3 trials [relative to other phases of RCTs] were associated with a significantly higher number of interim analyses which was also associated with a significantly higher number of regulatory approvals.

Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

Importance: Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, one of the barriers to the uptake of Bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared to a frequentist design. Objective: Compare the performance of a Bayesian and a frequentist adaptive clinical trial design. Design: Prospective observational study comparing two trial designs using individual patient level data from a completed stroke trial, including the timing and order of enrollments and outcome ascertainment. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses when 90-days post-randomization was met for 500, 700, 900, and 1,100 patients. The Bayesian alternative utilized predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients, and subsequent interims after every 100 randomizations. Setting: Multi-center, acute stroke study conducted within a National Institutes of Health neurological emergencies clinical trials network. Participants: Patient level data from 1,151 patients randomized in a clinical trial comparing intensive insulin therapy to standard in acute stroke patients with hyperglycemia. Main Outcome(s) and Measure(s): Sample size at end of study. This was defined as the sample size at which each of the studies stopped accrual of patients. Results: As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the Bayesian alternative crossed the futility boundary about 3 months earlier after 800 participants were randomized. Conclusions and Relevance: Both trial designs stopped for futility prior to reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the two designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how this was implemented between the two trials resulted in the differences in early stopping.

Platform Randomised trial of INterventions against COVID-19 In older peoPLE (PRINCIPLE): protocol for a randomised, controlled, open-label, adaptive platform, trial of community treatment of COVID-19 syndromic illness in people at higher risk

IntroductionThere is an urgent need to idenfy treatments for COVID-19 that reduce illness duration and hospital admission in those at higher risk of a longer illness course and complications.Methods and analysisThe Platform Randomised trial of INterventions against COVID-19 In older peoPLE trial is an open-label, multiarm, prospective, adaptive platform, randomised clinical trial to evaluate potential treatments for COVID-19 in the community. A master protocol governs the addition of new interventions as they become available, as well as the inclusion and cessation of existing intervention arms via frequent interim analyses. The first three interventions are hydroxychloroquine, azithromycin and doxycycline. Eligible participants must be symptomatic in the community with possible or confirmed COVID-19 that started in the preceding 14 days and either (1) aged 65 years and over or (2) aged 50–64 years with comorbidities. Recruitment is through general practice, health service helplines, COVID-19 ‘hot hubs’ and directly through the trial website. Participants are randomised to receive either usual care or a study drug plus usual care, and outcomes are collected via daily online symptom diary for 28 days from randomisation. The research team contacts participants and/or their study partner following days 7, 14 and 28 if the online diary is not completed. The trial has two coprimary endpoints: time to first self-report of feeling recovered from possible COVID-19 and hospital admission or death from possible COVID-19 infection, both within 28 days from randomisation. Prespecified interim analyses assess efficacy or futility of interventions and to modify randomisation probabilities that allocate more participants to interventions with better outcomes.Ethics and disseminationEthical approval Ref: 20/SC/0158 South Central - Berkshire Research Ethics Committee; IRAS Project ID: 281958; EudraCT Number: 2020-001209-22. Results will be presented to policymakers and at conferences and published in peer-reviewed journals.Trial registration numberISRCTN86534580.

Choosing Interim Sample Sizes in Group Sequential Designs

This manuscript investigates sample sizes for interim analyses in group sequential designs. Traditional group sequential designs (GSD) rely on “information fraction” arguments to define the interim sample sizes. Then, interim maximum likelihood estimators (MLEs) are used to decide whether to stop early or continue the data collection until the next interim analysis. The possibility of early stopping changes the distribution of interim and final MLEs: possible interim decisions on trial stopping excludes some sample space elements. At each interim analysis the distribution of an interim MLE is a mixture of truncated and untruncated distributions. The distributional form of an MLE becomes more and more complicated with each additional interim analysis. Test statistics that are asymptotically normal without a possibly of early stopping, become mixtures of truncated normal distributions under local alternatives. Stage-specific information ratios are equivalent to sample size ratios for independent and identically distributed data. This equivalence is used to justify interim sample sizes in GSDs. Because stage-specific information ratios derived from normally distributed data differ from those derived from non-normally distributed data, the former equivalence is invalid when there is a possibility of early stopping. Tarima and Flournoy [3] have proposed a new GSD where interim sample sizes are determined by a pre-defined sequence of ordered alternative hypotheses, and the calculation of information fractions is not needed. This innovation allows researchers to prescribe interim analyses based on desired power properties. This work compares interim power properties of a classical one-sided three stage Pocock design with a one-sided three stage design driven by three ordered alternatives.

Adaptive Learning of Drug Quality and Optimization of Patient Recruitment for Clinical Trials with Dropouts

Problem definition: Clinical trials are crucial to new drug development. This study investigates optimal patient enrollment in clinical trials with interim analyses, which are analyses of treatment responses from patients at intermediate points. Our model considers uncertainties in patient enrollment and drug treatment effectiveness. We consider the benefits of completing a trial early and the cost of accelerating a trial by maximizing the net present value of drug cumulative profit. Academic/practical relevance: Clinical trials frequently account for the largest cost in drug development, and patient enrollment is an important problem in trial management. Our study develops a dynamic program, accurately capturing the dynamics of the problem, to optimize patient enrollment while learning the treatment effectiveness of an investigated drug. Methodology: The model explicitly captures both the physical state (enrolled patients) and belief states about the effectiveness of the investigated drug and a standard treatment drug. Using Bayesian updates and dynamic programming, we establish monotonicity of the value function in state variables and characterize an optimal enrollment policy. We also introduce, for the first time, the use of backward approximate dynamic programming (ADP) for this problem class. We illustrate the findings using a clinical trial program from a leading firm. Our study performs sensitivity analyses of the input parameters on the optimal enrollment policy. Results: The value function is monotonic in cumulative patient enrollment and the average responses of treatment for the investigated drug and standard treatment drug. The optimal enrollment policy is nondecreasing in the average response from patients using the investigated drug and is nonincreasing in cumulative patient enrollment in periods between two successive interim analyses. The forward ADP algorithm (or backward ADP algorithm) exploiting the monotonicity of the value function reduced the run time from 1.5 months using the exact method to a day (or 20 minutes) within 4% of the exact method. Through an application to a leading firm’s clinical trial program, the study demonstrates that the firm can have a sizable gain of drug profit following the optimal policy that our model provides. Managerial implications: We developed a new model for improving the management of clinical trials. Our study provides insights of an optimal policy and insights into the sensitivity of value function to the dropout rate and prior probability distribution. A firm can have a sizable gain in the drug’s profit by managing its trials using the optimal policies and the properties of value function. We illustrated that firms can use the ADP algorithms to develop their patient enrollment strategies.