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Response adaptive randomization has the potential to treat more participants in better treatments in a trial to benefit participants. We propose optimal response adaptive randomization designs for a two-stage study with binary response, having the smallest expected sample size or the fewest expected number of failures. Equal randomization is used in the first stage, and data from the first stage is used to determine the adaptive sample size ratio in the second stage. In the proposed optimal designs, the type I error rate and the statistical power are calculated from the asymptotic normal distributions. The new designs that minimize the expected number of failures have the advantage over the existing optimal randomized designs to substantially reduce the number of failures.

In oral solid dosage production through direct compression powder lubrication must be carefully selected to facilitate the manufacturing of tablets without degrading product manufacturability and quality (e.g. dissolution). To do so, several semi-empirical models relating compression performance to process operating conditions have been developed. Among them, we consider an extension of the Kushner and Moore model (Kushner and Moore, 2010, International Journal Pharmaceutics, 399:19) that is useful for the purpose, but requires an extensive experimental campaign for parameters identification. This implies the preparation and compression of multiple powder blends, each one with a different lubrication extent. In turn, this translates into a considerable consumption of Active Pharmaceutical Ingredient (API), and into time-consuming experiments. We tackled this issue by proposing a novel model-based design of experiments (MBDoE) approach, which minimizes the number of optimal blends for model calibration, while obtaining statistically sound parameters estimates and model predictions. Both sequential and parallel MBDoE configurations were compared. Experimental results involving two placebo blends with different lubrication sensitivity showed that this methodology is able to reduce the experimental effort by 60-70% with respect to the standard industrial practice independently of the formulation considered and configuration (i.e. parallel vs. sequential) adopted., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Self-Organizing Maps (SOMs) are extensively used for data clustering and dimensionality reduction. However, if applications are to fully benefit from SOM based techniques, high-speed processing is demanding, given that data tends to be both highly dimensional and yet "big". Hence, a fully parallel architecture for the SOM is introduced to optimize the system's data processing time. Unlike most literature approaches, the architecture proposed here does not contain sequential steps - a common limiting factor for processing speed. The architecture was validated on FPGA and evaluated concerning hardware throughput and the use of resources. Comparisons to the state of the art show a speedup of 8.91× over a partially serial implementation, using less than 15% of hardware resources available. Thus, the method proposed here points to a hardware architecture that will not be obsolete quickly., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The reference scaled bioequivalence has been proposed with many successful applications for the highly variable products. The statistical properties for the reference scaled bioequivalence have been studied for the commonly used crossover design. However, a crossover design may not be feasible in a real application such as the biosimilar study, instead a parallel design is a more timely and cost-effective choice. In this paper, the approximate upper confidence interval limit for the linearized criteria in the reference scaled bioequivalence from a parallel design is derived. The performance of the approximation is evaluated through the simulation. The simulation results show that this approximation performs well and gives reasonable power and well-controlled type I error.

Pairing serves as a way of lessening heterogeneity but pays the price of introducing more parameters to the model. This complicates the probability structure and makes inference more intricate. We employ the simpler structure of the parallel design to develop a robust score statistic for testing the equality of two multinomial distributions in paired designs. This test incorporates the within-pair correlation in a data-driven manner without a full model specification. In the paired binary data scenario, the robust score statistic turns out to be the McNemar's test. We provide simulations and real data analysis to demonstrate the advantage of the robust procedure.

This paper introduces the revised cochrane risk of bias tool for individually randomized, parallel group trials (RoB2.0), compates RoB2.0 and the previous version (RoB1.0). And illustrates the application of RoB2.0 for a published clinical trial. As a comprehensive tool, RoB2.0 provides more information on the risk of bias for evidence synthesis. RoB2.0 is still under development and it is suggested that the users should follow the updates of the developers in the future.

Background: More than one-half of coronary artery disease (CAD) patients have low HDL cholesterol despite having well-managed LDL cholesterol. Almond supplementation has not been shown to elevate circulating HDL cholesterol concentrations in clinical trials, perhaps because the baseline HDL cholesterol of trial subjects was not low., Objective: This clinical trial was designed to test the effect of almond supplementation on low HDL cholesterol in CAD patients., Methods: A total of 150 CAD patients (50 per group), with serum LDL cholesterol ≤100 mg/dL and HDL cholesterol ≤40 mg/dL in men and ≤50 mg/dL in women, were recruited from the Aga Khan University Hospital. After recording vital signs and completing a dietary and physical activity questionnaire, patients were randomly assigned to 1 of the following 3 groups: the no-intervention group (NI), the Pakistani almonds group (PA), and the American almonds group (AA). The respective almond varieties (10 g/d) were given to patients with instructions to soak them overnight, remove the skin, and eat them before breakfast. Blood samples for lipid profiling, body weight, and blood pressure were collected, and assessment of dietary patterns was done at baseline, week 6, and week 12., Results: Almonds significantly increased HDL cholesterol. At weeks 6 and 12, HDL cholesterol was 12-14% and 14-16% higher, respectively, in the PA and AA than their respective baselines. In line with previous reports, serum concentrations of total cholesterol, triglycerides, LDL cholesterol, and VLDL cholesterol; total-to-HDL and LDL-to-HDL cholesterol ratios, and the atherogenic index were reduced in both the PA and AA at weeks 6 and 12 compared with baseline (P < 0.05). Effects on serum lipids did not differ between the 2 almond groups. Dietary patterns, body weight, and blood pressure did not change in any of the 3 groups during the trial., Conclusion: A low dose of almonds (10 g/d) consumed before breakfast can increase HDL cholesterol, in addition to improving other markers of abnormal lipid metabolism in CAD patients with low initial HDL cholesterol. This trial was registered at the Australian New Zealand Clinical Trial Registry as ACTRN12614000036617., (© 2015 American Society for Nutrition.)

It is common in educational and psychological tests or social surveys that the same statement is judged on multiple scales. These multiple responses are linked by the same statement, which may cause local dependence. Considering the way a statement is judged on multiple scales, a new class of item response theory (IRT) models is developed to account for the nonrecursive carry-over effect, in which a response can be affected only by its preceding response rather than by a subsequent response. The parameters of the models can be estimated with the freeware WinBUGS. Two simulation studies were conducted to evaluate the parameter recovery of the new models and the consequences of model misspecification. Results showed that the parameters of the new models were recovered fairly well; fitting unnecessarily complicated models to data that did not have the carry-over effect did little harm to parameter estimation; and ignoring the carry-over effect by fitting standard IRT models yielded biased estimates for the item parameters, the correlation between latent traits, and the test reliability. Two empirical examples with parallel design and sequential design are provided to demonstrate the implications and applications of the new models., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The objective of the study was to identify all parallel design randomised controlled trials (RCTs) comparing treatments for eczema in recent dermatology literature that have failed to report a between-group analysis. The GREAT database (www.greatdatabase.org.uk) was searched to identify parallel group RCTs comparing two or more interventions published in the English language in the last decade, 2004 to 2013. The primary outcome was the number of studies that had not reported a between-group analysis for any of the outcomes. Where possible we re-analysed the data to determine whether a between-group analysis would have given a different conclusion to that reported. Out of a total of 304 RCTs in the study period, 173 (56.9%) met the inclusion criteria. Of the 173 eligible studies, 12 (6.9%) had not conducted a between-group analysis for any of the reported outcomes. There was no clear improvement over time. Five of the eight studies that were re-analysed yielded non-significant between-group differences yet reported significant within-group comparisons. All but one of the 12 studies implied that the experimental intervention was successful despite not undertaking any between-group comparisons. Although the proportion of all RCTs that fail to report an appropriate between-group analysis is small, the fact that any scientist who purports to compare one treatment against another then chooses to omit the key comparison statistic is worrying.

For thorough QT (TQT) studies employing a parallel-group design, there has been a clear regulatory preference for the use of time-matched, rather than time-averaged, baseline values to account for circadian variation when estimating the magnitude of the drug effect on the QT interval. In this paper, both historical data from parallel-group TQT studies and simulated data from assumed circadian models are utilized to comprehensively assess the performance characteristics of 3 repeated-measures analysis of covariance models. The results indicate that each analysis model performs adequately in the absence of an observed time-matched baseline imbalance between the treatment groups. However, the analysis model with time-matched baseline as a covariate performs poorly under the setting of an observed time-matched baseline imbalance between the treatment groups. The analysis model with time-averaged baseline as a covariate and the analysis model with both time-matched and time-averaged baselines as covariates provide unbiased estimates of the treatment difference and properly control the type I error rate, regardless of an observed time-matched baseline imbalance or within-patient variation in circadian parameters.