scholarly journals Scalable Algorithms for Adaptive Statistical Designs

2000 ◽  
Vol 8 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Robert Oehmke ◽  
Janis Hardwick ◽  
Quentin F. Stout
Keyword(s):  
Clinical Trials ◽  
High Performance ◽  
Learning Algorithms ◽  
Adaptive Designs ◽  
Memory Access ◽  
Important Class ◽  
Stochastic Environment ◽  
Scalable Algorithms ◽  
Nested Loops ◽  
Statistical Designs

We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include massive memory requirements, few calculations per memory access, and multiply-nested loops with dynamic indices. We analyze the effects of various parallelization options, and while standard approaches do not work well, with effort an efficient, highly scalable program can be developed. This allows us to solve problems thousands of times more complex than those solved previously, which helps make adaptive designs practical. Further, our work applies to many other problems involving neighbor recurrences, such as generalized string matching.

Translation of Innovative Designs Into Phase I Trials

2007 ◽  
Vol 25 (31) ◽  
pp. 4982-4986 ◽  
Author(s):  
André Rogatko ◽  
David Schoeneck ◽  
William Jonas ◽  
Mourad Tighiouart ◽  
Fadlo R. Khuri ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Well Being ◽  
Adaptive Designs ◽  
Dose Finding ◽  
Therapeutic Window ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Statistical Studies ◽  
Statistical Designs

Purpose Phase I clinical trials of new anticancer therapies determine suitable doses for further testing. Optimization of their design is vital in that they enroll cancer patients whose well-being is distinctly at risk. This study examines the effectiveness of knowledge transfer about more effective statistical designs to clinical practice. Methods We examined abstract records of cancer phase I trials from the Science Citation Index database between 1991 and 2006 and classified them into clinical (dose-finding trials) and statistical trials (methodologic studies of dose-escalation designs). We then mapped these two sets by tracking which trials adopted new statistical designs. Results One thousand two hundred thirty-five clinical and 90 statistical studies were identified. Only 1.6% of the phase I cancer trials (20 of 1,235 trials) followed a design proposed in one of the statistical studies. These 20 clinical studies showed extensive lags between publication of the statistical paper and its translation into a clinical paper. These 20 clinical trials followed Bayesian adaptive designs. The remainder used variations of the standard up-and-down method. Conclusion A consequence of using less effective designs is that more patients are treated with doses outside the therapeutic window. Simulation studies have shown that up-and-down designs treated only 35% of patients at optimal dose levels versus 55% for Bayesian adaptive designs. This implies needless loss of treatment efficacy and, possibly, lives. We suggest that regulatory agencies (eg, US Food and Drug Administration) should proactively encourage the adoption of statistical designs that would allow more patients to be treated at near-optimal doses while controlling for excessive toxicity.

Testing hypotheses under adaptive randomization with continuous covariates in clinical trials

2018 ◽  
Vol 28 (6) ◽  
pp. 1609-1621
Author(s):  
Xiaoming Li ◽  
Jianhui Zhou ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Hypothesis Testing ◽  
Treatment Effects ◽  
Linear Models ◽  
Adaptive Designs ◽  
Cumulative Distribution ◽  
Type I ◽  
Percentile Method ◽  
Continuous Covariates ◽  
Leibler Divergence

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su’s percentile method, the empirical cumulative-distribution method, the Kullback–Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid.

scholarly journals NUMA-Aware DGEMM Based on 64-Bit ARMv8 Multicore Processors Architecture

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1984
Author(s):  
Wei Zhang ◽  
Zihao Jiang ◽  
Zhiguang Chen ◽  
Nong Xiao ◽  
Yang Ou
Keyword(s):  
Energy Efficiency ◽  
High Performance ◽  
Multicore Processors ◽  
Matrix Multiplication ◽  
Memory Access ◽  
Double Precision ◽  
Competitive Performance ◽  
General Matrix ◽  
Remarkable Improvement ◽  
Task Independence

Double-precision general matrix multiplication (DGEMM) is an essential kernel for measuring the potential performance of an HPC platform. ARMv8-based system-on-chips (SoCs) have become the candidates for the next-generation HPC systems with their highly competitive performance and energy efficiency. Therefore, it is meaningful to design high-performance DGEMM for ARMv8-based SoCs. However, as ARMv8-based SoCs integrate increasing cores, modern CPU uses non-uniform memory access (NUMA). NUMA restricts the performance and scalability of DGEMM when many threads access remote NUMA domains. This poses a challenge to develop high-performance DGEMM on multi-NUMA architecture. We present a NUMA-aware method to reduce the number of cross-die and cross-chip memory access events. The critical enabler for NUMA-aware DGEMM is to leverage two levels of parallelism between and within nodes in a purely threaded implementation, which allows the task independence and data localization of NUMA nodes. We have implemented NUMA-aware DGEMM in the OpenBLAS and evaluated it on a dual-socket server with 48-core processors based on the Kunpeng920 architecture. The results show that NUMA-aware DGEMM has effectively reduced the number of cross-die and cross-chip memory access, resulting in enhancing the scalability of DGEMM significantly and increasing the performance of DGEMM by 17.1% on average, with the most remarkable improvement being 21.9%.

scholarly journals Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

2021 ◽  
Author(s):  
Elja Arjas ◽  
Dario Gasbarra
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Statistical Significance ◽  
Adaptive Designs ◽  
Critical Threshold ◽  
Attractive Alternative ◽  
Posterior Probabilities ◽  
Statistical Significance Testing ◽  
Treatment Allocation ◽  
Frequentist Paradigm

Abstract Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

Environmentally friendly thermoelectric sulphide Cu2ZnSnS4 single crystals with dimensionless figure of merit ZT achieving 1.6

2021 ◽  
Author(s):  
Akira Nagaoka ◽  
Kenji Yoshino ◽  
Taizo Masuda ◽  
Taylor D. Sparks ◽  
Michael A. Scarpulla ◽  
...  
Keyword(s):  
Electric Power ◽  
Single Crystals ◽  
High Performance ◽  
Figure Of Merit ◽  
Environmentally Friendly ◽  
Heat Sources ◽  
Important Class ◽  
Dimensionless Figure Of Merit ◽  
Cubic Structure

Thermoelectrics (TEs) are an important class of technologies for harvesting electric power directly from heat sources. To design both high performance and environmentally friendly for TE materials, pseudo-cubic structure has...

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