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Statistical Methods for Assessment of Biosimilars
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
Yafei Zhang, Vivian Gu, Xiuyu Julie Cong, Shein-Chung Chow
The United States FDA, on the other hand, suggests pairwise comparisons should be performed. FDA's recommendation three comparisons, namely, (i) BP vs US, (ii) BP vs EU, and (iii) US vs EU. As indicated by the ODAC (held on 13 July 2017), FDA's recommended method of pairwise comparison suffers from the following limitations: (i) pairwise comparison does not use the same reference product in the comparison (i.e., these comparisons use different similarity margins), and (ii) pairwise comparison does not utilize all data collected from the test and two reference groups. In addition, pairwise comparison may not be able to detect the following possible relationship (pattern) among BP, US, and EU under the three-arm parallel-group design: (i) US > BP > EU, (ii) US > EU > BP, (iii) BP > US > EU, (iv) BP > EU > US, (v) EU > BP > US, and (vi) EU > US > BP.
Hypotheses Testing versus Confidence Interval
Published in Shein-Chung Chow, Innovative Statistics in Regulatory Science, 2019
In comparative clinical trials, it is not uncommon to have multiple controls (or references). For example, for assessment of biosimilarity between a proposed biosimilar product (test product) and an innovative biological product (reference product), there may be multiple references, e.g., a US-licensed reference product and an EU-approved reference version of the same product. In this case, the method of pairwise comparisons is often applied. When two reference products (e.g., a US-licensed reference and an EU-approved reference) are considered, the method of pairwise comparisons includes three comparisons (i.e., the proposed biosimilar product versus the US-licensed reference product, the proposed biosimilar product versus the EU-approved reference product, and the US-licensed reference product versus the EU-approved reference product).
Special Issues and Resolutions
Published in Mark Chang, John Balser, Jim Roach, Robin Bliss, Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
Mark Chang, John Balser, Jim Roach, Robin Bliss
The second approach, drop-loser design with gatekeeping procedure (DLDGKP), is proposed by Deng and Chang (2018) to improve efficiency of the design by sacrificing some not very useful flexibility, that is, allowing for any number of analyses performed at any time no later than τmax to drop any number of arms based on flexible criteria don’t have to be fully prespecified, but a non-negative correlation is required between the final test statistic and the statistic at the IA for dropping the losers. Furthermore, at the final analysis, the hypothesis testing for the pairwise comparisons will be performed in a gatekeeping fashion as described below. We assume the drop-loser decisions are made by the independent data monitor committee (IDMC).
Implementation of Clean Hospital Strategy and Prioritizing Covid-19 Prevention Factors Using Best-Worst Method
Published in Hospital Topics, 2023
Bahareh Ahmadinejad, Amir Shabani, Alireza Jalali
Best Worst Method (BWM) is one of the popular and latest MCDM methods that researchers and professionals use to address their decision-making challenges. This approach is based on pairwise comparison between the better criterion to the other criteria and the worst criterion for all the other criteria (Rezaei 2016). For the purpose of weighting criteria, there are many MCDM methods including, simple multi-attribute rating technique (SMART), Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), etc., but BWM has many notable benefits such as (Rezaei 2016; Mi et al. 2019):Fewer pairwise comparisons are needed by the BWM in comparison with the AHP and the other MCDM methods.Due to its nature and absence of redundant pairwise comparisons, it produces more coherent and accurate outcomes.The decision-maker only utilizes integer values for pairwise comparisons in this process, which is more intuitive compared to fractions.
High-sugar, high-fat, and high-protein diets promote antibiotic resistance gene spreading in the mouse intestinal microbiota
Published in Gut Microbes, 2022
Rong Tan, Min Jin, Yifan Shao, Jing Yin, Haibei Li, Tianjiao Chen, Danyang Shi, Shuqing Zhou, Junwen Li, Dong Yang
GraphPad Prism V7.0 (https://www.graphpad.com/) and RStudio (https://www.rstudio.com/) were used for statistical analysis and mapping. Body weight, fecal excretion, and immune factors of mice in each group were analyzed by unpaired t-tests, Dunnett’s multiple comparison tests, or one-way analysis of variance (ANOVA) of Tukey’s multiple comparison tests. Statistical significance was set at 0.05, hence p > .05 means no statistical significance. The significance of pairwise comparisons is indicated by asterisks (*p < .05, **p < .01, ***p< .001). In α-diversity analysis, qiime software (v2.0, http://qiime2.org/) was used to calculate Chao1 and Shannon indices. Difference analysis of the α-diversity index between groups was conducted with parametric and nonparametric tests. Since the experiment involved more than two groups, Tukey and Wilcox tests were performed. For β-diversity, the ggplot2 (V4.05) program in the R software package (https://www.r-project.org/) was used to draw a principal coordinate analysis (PCoA) diagram, heatmap, and overview circle diagram. R software was used to analyze differences between groups for β-diversity, and parametric and nonparametric tests were carried out. The Anosim, MRPP, and Adonis functions of the R vegan package were also used.
Applying social cognitive theory to nonsuicidal self-injury: Interactions between expectancy beliefs
Published in Journal of American College Health, 2022
Jessica C. Dawkins, Penelope A. Hasking, Mark E. Boyes
Missing values analysis revealed that data were not missing completely at random p < .001, however, as items were missing between 0–1.2%of data Expectation Maximization was used to impute data.26 Of the full sample, 82 individuals (16.4%) reported NSSI ideation but no engagement in NSSI, 74 individuals (14.8%) reported engaging in NSSI but not in the past 12 months, and 116 individuals (23.2%) reported having engaged in NSSI within the past 12 months. The mean age of onset was 13.81 years old and the most common main form of NSSI was cutting (48.7%) followed by self-battery (13.4%) and severe scratching (11.4%). Correlations between all variables can be found in Table 1. Table 2 shows group differences in expectations of affect regulation, negative social outcomes, communication, and pain as well as all measured facets of self-efficacy to resist NSSI. Pairwise comparisons are also reported.