Deley, Marie‐Cécile Le

2577 Background: The emergence of MTA in oncology has revolutionized the phase I design paradigm. The usual dose-limiting toxicity (DLT) endpoint is an oversimplification of the complex toxicity profile resulting in loss of information. Moreover, MTAs can induce less DLTs but more multiple moderate toxicities that may lead to dose reduction in long lasting treatment. We thus sought to develop a new toxicity endpoint, the Total Toxicity Profile (TTP), to incorporate multiple toxicities. Methods: We reanalysed the phase I trial that evaluated erlotinib (75, 100, 125, 150 mg/m2) in children with brain stem glioma (NCT00418327). Only 2/21 patients experienced protocol defined DLTs, but multiple moderate toxicities were observed. The TTP endpoint is a weighted combination of all toxicities experienced by the patient (Euclidean norm of the weights), where the weights (ranging from 0 to 10) reflect the clinical importance of each grade and type of toxicity. The weights and the acceptable toxicity target (target TTP) were elicited from 3 clinicians using hypothetical patient cohorts with various toxicity outcomes. They were asked to make a decision of where the next cohort of patients would be treated: higher, lower or same dose level. The target TTP was defined as the mean of TTPs of the cohorts associated with a decision to repeat the dose. We used the TTP-driven Quasi-Likelihood Continual Reassessment Method (QL-CRM) to re-estimate the recommended dose. Results: Clinicians identified 5 main dermatological toxicity types possibly attributable to erlotinib. The consensus weights (table below) as well as the target TTP (set at 4.66) were easily obtained, confirming the feasibility of the process. The weights differed from the CTCAE grades, confirming the interest of the approach. The recommended dose based on the retrospective analysis of all grades toxicity was lower than that identified by the DLT-driven CRM (100 instead of 125mg/m2). Conclusions: By neglecting multiple moderate toxicities, DLT-driven designs may over-estimate the recommended dose when investigating MTA. Our proposal is an appealing alternative design for MTA. [Table: see text]

Large sample sizes are required in randomized clinical trials designed to meet typical one‐sided 2.5% α‐level and 80% power. This may not be achievable when the disease is rare. We simulated a series of two‐arm superiority trials over a 15‐year period. The design parameters examined were the α‐level and the number of trials conducted over the 15‐year period (thus, trial sample size). Different disease severities and accrual rates were considered. The future treatment effect was characterized by its associated hazard rate; different hypotheses of how treatments improve over time were considered. We defined the total survival benefit as the relative difference of the hazard rates at year 15 versus year 0. The optimal design was defined by maximizing the expected total survival benefit, provided that the risk of selecting at year 15 a treatment inferior to the initial control treatment remains below 1%. Compared with two larger trials with typical one‐sided 2.5% α‐level, performing a series of small trials with relaxed α‐levels leads on average to larger survival benefits over a 15‐year research horizon, but also to higher risk of selecting a worse treatment at the end of the research period. Under reasonably optimistic assumptions regarding the future treatment effects, optimal designs outperform traditional ones when the disease is severe (baseline median survival ≤ 1 year) and the accrual is ≥100 patients per year, whereas no major improvement is observed in diseases with better prognosis. Trial designs aiming to maximize survival gain over a long research horizon across a series of trials are worth discussing in the context of rare diseases. Copyright © 2016 John Wiley & Sons, Ltd.

The aim of a phase I oncology trial is to identify a dose with an acceptable safety profile. Most phase I designs use the dose‐limiting toxicity, a binary endpoint, to assess the unacceptable level of toxicity. The dose‐limiting toxicity might be incomplete for investigating molecularly targeted therapies as much useful toxicity information is discarded. In this work, we propose a quasi‐continuous toxicity score, the total toxicity profile (TTP), to measure quantitatively and comprehensively the overall severity of multiple toxicities. We define the TTP as the Euclidean norm of the weights of toxicities experienced by a patient, where the weights reflect the relative clinical importance of each grade and toxicity type. We propose a dose‐finding design, the quasi‐likelihood continual reassessment method (CRM), incorporating the TTP score into the CRM, with a logistic model for the dose–toxicity relationship in a frequentist framework. Using simulations, we compared our design with three existing designs for quasi‐continuous toxicity score (the Bayesian quasi‐CRM with an empiric model and two nonparametric designs), all using the TTP score, under eight different scenarios. All designs using the TTP score to identify the recommended dose had good performance characteristics for most scenarios, with good overdosing control. For a sample size of 36, the percentage of correct selection for the quasi‐likelihood CRM ranged from 80% to 90%, with similar results for the quasi‐CRM design. These designs with TTP score present an appealing alternative to the conventional dose‐finding designs, especially in the context of molecularly targeted agents. Copyright © 2013 John Wiley & Sons, Ltd.

Background Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible. Purpose To compare the performance of different trial design strategies over a long-term research horizon. Methods We simulated a series of two-treatment superiority trials over 15 years using different design parameters. Trial parameters examined included the number of positive trials to establish superiority (one-trial vs. two-trial rule), α level (2.5%–50%), and the number of trials in the 15-year period, K (thus, trial sample size). The design parameters were evaluated for different disease scenarios, accrual rates, and distributions of treatment effect. Metrics used included the overall survival gain at a 15-year horizon measured by the hazard ratio (HR), year 15 versus year 0. We also computed the expected total survival benefit and the risk of selecting as new standard of care at year 15 a treatment inferior to the initial control treatment, P(detrimental effect). Results Expected survival benefits over the 15-year horizon were maximized when more (smaller) trials were conducted than recommended under traditional criteria, using the criterion of one positive trial (vs. two), and relaxing the α value from 2.5% to 20%. Reducing the sample size and relaxing the α value also increased the likelihood of selecting an inferior treatment at the end. The impact of α and K on the survival benefit depended on the specific disease scenario and accrual rate: greater gains for relaxing α in diseases with good outcome and/or low accrual rates and greater gains for increasing K for diseases with poor outcomes. Trials with smaller sample size did not perform well when using stringent (standard) level of evidence. For each disease scenario and accrual rate studied, the optimal design, defined as the design that the maximized expected total survival benefit while constraining P(detrimental effect) < 2.5%, specified α = 20% or 10%, and a sample size considerably smaller than that recommended by the traditional designs. The results were consistent under different assumed distributions for treatment effect. Limitations The simulations assumed no toxicity issues and did not consider interim analyses. Conclusions It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today’s environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.