Article
Mathematical & Computational Biology
Xin Li, Wei Ma, Feifang Hu
Summary: Combining Covariate-adaptive randomization (CAR) with sample size re-estimation (SSR) in clinical trials has become increasingly popular due to its advantages in statistical efficiency and cost reduction. However, adjustments are necessary to protect the accuracy of the combined design, and this article provides a framework for the application of SSR in CAR trials and studies the underlying theoretical properties. Numerical studies show that the advantages of CAR and SSR can be further improved in terms of power and sample size.
STATISTICS IN MEDICINE
(2021)
Article
Mathematical & Computational Biology
Peijin Wang, Shein-Chung Chow
Summary: This article discusses methods for sample size re-estimation in clinical trials, including the adjusted effect size (AES) approach and the iterated expectation/variance (IEV) approach, which take into account the variability of observed responses. Results show that the IEV approach generally performs best in controlling type I error inflation, but may lead to a larger increase in sample size when detecting smaller effect sizes.
STATISTICS IN MEDICINE
(2021)
Article
Pharmacology & Pharmacy
Xin Li, Feifang Hu
Summary: This article proposes a sample size re-estimation procedure for response-adaptive randomized trials. By using multiple stopping criteria and allowing for early termination, the power of the trials is increased, sample size is reduced, and the duration of trials is shortened.
PHARMACEUTICAL STATISTICS
(2022)
Article
Medicine, Research & Experimental
Ruitao Lin, Zhao Yang, Ying Yuan, Guosheng Yin
Summary: The heterogeneity of clinical trial participants poses a fundamental challenge in the field of precision medicine, but adaptive enrichment designs offer a flexible and intuitive solution. By enriching the subgroup of trial participants with a higher likelihood of benefit from a new treatment, these designs can control type I error rate and improve statistical power and expected sample size.
CONTEMPORARY CLINICAL TRIALS
(2021)
Article
Pharmacology & Pharmacy
Maximilian Pilz, Carolin Herrmann, Geraldine Rauch, Meinhard Kieser
Summary: Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we propose an approach to construct adaptive designs with defined features by solving an optimization problem for unplanned design reassessment. By using the conditional error principle, we present a method to optimally modify the trial design at an unplanned interim analysis while protecting the type I error rate.
PHARMACEUTICAL STATISTICS
(2022)
Article
Mathematical & Computational Biology
Nicolas M. Ballarini, Thomas Burnett, Thomas Jaki, Christoper Jennison, Franz Koenig, Martin Posch
Summary: We propose a two-stage confirmatory clinical trial design that uses adaptation to identify the subgroup of patients benefiting from a new treatment. The study implements adaptations using the conditional error rate approach while optimizing design parameters based on a utility function considering the population prevalence of the subgroups. Results are shown for both traditional trials with familywise error rate control and umbrella trials with control only over the per-comparison type 1 error rate.
STATISTICS IN MEDICINE
(2021)
Article
Mathematical & Computational Biology
Liwen Wu, Qing Li, Mengya Liu, Jianchang Lin
Summary: Adaptive subgroup enrichment design is an efficient framework that allows accelerated development for investigational treatments and offers flexibility in population selection. This article improves the interim decision making process by incorporating information from surrogate endpoints and prior knowledge, aiming to overcome the challenge of immature data in adaptive designs.
STATISTICS IN BIOPHARMACEUTICAL RESEARCH
(2022)
Article
Mathematical & Computational Biology
Qi Zhang, Yuqian Shen, Hui Quan, Pascal Minini, Lin Wang
Summary: Due to the need to accelerate the drug development process in rare disease areas, a two-stage adaptive design option is being evaluated for a placebo-controlled registration study. The study involves participants from an ongoing phase 2 study for stage 1 and newly enrolled participants for stage 2, with different treatment periods. The primary endpoint is the rate of change for a continuous measurement, which will be evaluated using a mixed model. Interim analyses will be conducted to adjust sample size and assess early efficacy.
STATISTICS IN BIOPHARMACEUTICAL RESEARCH
(2023)
Article
Medicine, Research & Experimental
Yi Liu, Heng Xu
Summary: The paper discusses three main approaches to address the issue of inflated Type I error rates in sample size re-estimation designs, namely combination test, conditional error, and conventional test with sample size increase in the allowable region. Although traditional test guarantees Type I error rate control, it often results in lower power compared to the corresponding combination test approach.
CONTEMPORARY CLINICAL TRIALS
(2021)
Article
Engineering, Environmental
Ali Jozaghi, Haojing Shen, Dong-Jun Seo
Summary: Accurate spatial estimation of extremes is crucial in environmental research and risk assessment. This paper introduces adaptive conditional bias-penalized kriging, which objectively prescribes weights to improve estimation of extremes without compromising performance.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Review
Geriatrics & Gerontology
Graziella D'Arrigo, Stefanos Roumeliotis, Claudia Torino, Giovanni Tripepi
Summary: A crucial step in planning a randomized clinical trial (RCT) is the calculation of sample size, which determines the optimal number of patients needed to ensure the study has enough power to detect differences in specific endpoints between study arms. This calculation involves inputting variables such as the expected effect size, alpha error (α), beta error (β), and the allocation ratio in order to determine the number of participants allocated to each arm of the RCT.
AGING CLINICAL AND EXPERIMENTAL RESEARCH
(2021)
Editorial Material
Oncology
Alberto Hernando-Calvo, Elena Garralda
Summary: The heterogeneity of cancer molecular profiles and the variability of anticancer responses has led to the need for personalized therapies. However, the development of effective combinations of targeted therapies faces various challenges, including biomarker discovery, rational combination selection, additive toxicities, and regulatory requirements. Despite an increase in preclinical publications exploring combinations, only a few have reached routine clinical care.
Article
Mathematics
Yanchun Zhao, Mengzhu Zhang, Qian Ni, Xuhui Wang
Summary: Learning density estimation is crucial for probabilistic modeling and reasoning with uncertainty. B-spline basis functions provide advantages in density estimation due to their local support and numerical computation efficiency. However, selecting the bandwidth for uniform B-splines is challenging and computationally expensive. In this study, we propose an adaptive strategy for density estimation using nonuniform B-splines by introducing an error indicator attached to each interval, which is an approximation of the local information entropy. Our numerical experiments demonstrate that the local density estimation with nonuniform B-splines outperforms the uniform B-spline, achieving better estimation results and alleviating the overfitting phenomenon.
Article
Engineering, Civil
Yiming Hu, Zhongmin Liang, Yixin Huang, Yi Yao, Jun Wang, Binquan Li
Summary: A framework is proposed to estimate the nonstationary bivariate design flood, which includes constructing a nonstationary copula model, using the equivalent reliability method to calculate design values and design lifespan length, and calculating the design value of the secondary variable using conditional most likely combination and conditional expectation combination strategies. A case study shows the design value combination of the nonstationary bivariate and its temporal variation.
JOURNAL OF HYDROLOGY
(2022)
Article
Automation & Control Systems
Minh-Lien Jeanne Nguyen, Claire Lacour, Vincent Rivoirard
Summary: This paper studies the estimation of the conditional density given observation data and an independent identically distributed sample. It provides an adaptive fully-nonparametric strategy based on kernel rules, and proposes a new fast iterative algorithm to select the bandwidth of the kernel rule. The results show that the pointwise estimator achieves quasi-optimal convergence rate in terms of both regularity and sparsity, and the computational complexity of the method is only O(dn log n).
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
