A stochastically curtailed single‐arm phase II trial design for binary outcomes
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Abstract:
Phase II clinical trials are a critical aspect of the drug development process. With drug development costs ever increasing, novel designs that can improve the efficiency of phase II trials are extremely valuable.Phase II clinical trials for cancer treatments often measure a binary outcome. The final trial decision is generally to continue or cease development. When this decision is based solely on the result of a hypothesis test, the result may be known with certainty before the planned end of the trial. Unfortunately, there is often no opportunity for early stopping when this occurs.Some existing designs do permit early stopping in this case, accordingly reducing the required sample size and potentially speeding up drug development. However, more improvements can be achieved by stopping early when the final trial decision is very likely, rather than certain, known as stochastic curtailment. While some authors have proposed approaches of this form, these approaches have various limitations.In this work we address these limitations by proposing new design approaches for single-arm phase II binary outcome trials that use stochastic curtailment. We use exact distributions, avoid simulation, consider a wider range of possible designs and permit early stopping for promising treatments. As a result, we are able to obtain trial designs that have considerably reduced sample sizes on average.Keywords:
Early stopping
Drug Development
Multiregional clinical trials including Japanese subjects are playing a key role in new drug development in Japan. In addition to the consideration of differences in intrinsic and extrinsic ethnic factors, deciding the sample size of Japanese subjects is an important issue when a multiregional clinical trial is intended to be used for Japanese submission. Accumulated experience suggests that there are several points to consider, such as the basic principles described in the guidance document, drug development strategy, trial phase, and disease background. The difficulty of interpreting the results of Japanese trials should also be considered.
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In recent years, seamless phase I/II clinical trials have drawn much attention, as they consider both toxicity and efficacy endpoints in finding an optimal dose (OD). Engaging an appropriate number of patients in a trial is a challenging task. This paper attempts a dynamic stopping rule to save resources in phase I/II trials. That is, the stopping rule aims to save patients from unnecessary toxic or subtherapeutic doses. We allow a trial to stop early when widths of the confidence intervals for the dose-response parameters become narrower or when the sample size is equal to a predefined size, whichever comes first. The simulation study of dose-response scenarios in various settings demonstrates that the proposed stopping rule can engage an appropriate number of patients. Therefore, we suggest its use in clinical trials.
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Abstract Pharmaceutical companies incorporate different features into the trials for new drug applications (NDAs) to render them efficient, making use of their experience. The objective of this analysis was to examine the associations between outcome and features related to study design and clinical development experience in commercially sponsored clinical trials. We collected data of phase 2 and phase 3 trials of all the drugs that obtained approval for depression, schizophrenia, asthma, hypertension, and diabetes in Japan from 1970 to 2011. In total, 145 trials from 90 test drugs were eligible for our study. We calculated the effect size, the standard mean of differences between test drug and comparator therapeutic effects, as the objective variable for use in our analysis. A linear mixed effect model with nested and crossed random effects was used in the analysis including variety of therapeutic area, test drugs and clinical trials. The analysis showed that trial features including sample size, subjective endpoints and active comparator of the same mode of action were negatively associated with effect size. In addition, sponsors’ domestic clinical development experience with similar drugs seemed to have a positive association, but prior development experience in foreign countries did not. The accumulation of skills and knowledge within sponsors, and accumulated experience in domestic professionals who implement clinical trials under study contracts with sponsors would be of great importance for yielding clear outcomes. This study provides additional evidence with respect to possible sizes and directions of the influence of study design features that must be considered when planning and implementing trials for new drug applications, and when retrospectively comparing outcomes from trials with different designs and environments.
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We propose a two‐stage design for a single arm clinical trial with an early stopping rule for futility. This design employs different endpoints to assess early stopping and efficacy. The early stopping rule is based on a criteria determined more quickly than that for efficacy. These separate criteria are also nested in the sense that efficacy is a special case of, but usually not identical to, the early stopping endpoint. The design readily allows for planning in terms of statistical significance, power, expected sample size, and expected duration. This method is illustrated with a phase II design comparing rates of disease progression in elderly patients treated for lung cancer to rates found using a historical control. In this example, the early stopping rule is based on the number of patients who exhibit progression‐free survival (PFS) at 2 months post treatment follow‐up. Efficacy is judged by the number of patients who have PFS at 6 months. We demonstrate our design has expected sample size and power comparable with the Simon two‐stage design but exhibits shorter expected duration under a range of useful parameter values.
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In this paper, we present a novel algorithm to address the Network Alignment problem. It is inspired from a previous message passing framework of Bayati et al. [2] and includes several modifications designed to significantly speed up the message updates as well as to enforce their convergence. Experiments show that our proposed model outperforms other state-of-the-art solvers. Finally, we propose an application of our method in order to address the Binary Diffing problem. We show that our solution provides better assignment than the reference differs in almost all submitted instances and outline the importance of leveraging the graphical structure of binary programs.
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Abstract Background Although there are accelerated approval pathways based on the data of small populations and surrogate endpoints, the concern that these pathways authorize the use of inefficacious drugs based on limited data from earlier phase clinical trials remains. We retrospectively investigated the efficacy of anticancer drugs in small and large clinical trials and verified whether small clinical trials could reflect the efficacy results of large clinical trials. Design: All anticancer drugs approved in Japan or whose development was terminated from 2015 to 2019 were searched. The median overall survival (OS), median progression-free survival (PFS), and overall response rates (ORR) between small clinical trials (sample size ≤100) and large clinical trials (sample size >100) with identical target populations and treatment settings were compared. Simple linear regression and Spearman’s correlation analyses were performed. Results A total of 61 comparable small and large clinical trials were identified. For all endpoints, significant linear trend and correlation were detected ( P < 0.001). In particular, the correlation coefficients of median OS and ORR were high (0.852 and 0.873, respectively). Conclusion The median OS, median PFS, and ORR data of small clinical trials precisely reflected the efficacy data of large clinical trials. Thus, the efficacy data of small clinical trials can be used to predict the efficacy data of a large population. These results support the accelerated approval pathway based on promising efficacy data of small populations in anticancer drug development.
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