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The article discusses the application and interpretability of predictive statistical data analysis methods in medicine. It highlights the importance of understanding medical phenomena and intervening to change outcomes, rather than solely focusing on prediction. The article acknowledges the potential of artificial intelligence (AI) methods in medicine, but also raises concerns about the lack of transparency and interpretability in predictive algorithms. It explores various methods, such as explainable AI (XAI) and visualizations, that aim to address these challenges. The article also emphasizes the need for combining different analysis methods, such as regression models and machine learning, to obtain reliable and interpretable results. Additionally, it discusses the role of meta-analysis in evidence-based medicine and the challenges of synthesizing results from predictive models. Overall, the article provides insights into the complexity and potential of predictive statistical data analysis methods in medicine. [Extracted from the article]

The lack of consistent presentation of results in published studies on the association between a quantitative explanatory variable and a quantitative dependent variable has been a long-term issue in evaluating the reported findings. Studies are analyzed and reported in a variety of ways. The main purpose of this review is to illustrate the procedures in summarizing and synthesizing research results from multivariate models with a quantitative outcome variable. The review summarizes the application of the standardized regression coefficient as an effect size index in the context of meta-analysis and describe how it can be estimated and converted from data presented in original research articles. An example of synthesis is provided using research articles on the association between childhood body mass index and carotid intima-media thickness in adult life. Finally, the paper shares practical recommendations for meta-analysts wanting to use the standardized regression coefficient in pooling findings. [ABSTRACT FROM AUTHOR]

Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in 'predatory dental journals' and in other dental journals. We evaluated 50 articles published in 'predatory open access (OA) journals' and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review. [ABSTRACT FROM AUTHOR]

Although the sizes of business firms have been a subject of intensive research, the definition of a "size" of a firm remains unclear. In this study, we empirically characterize in detail the scaling relations between size measures of business firms, analyzing them based on allometric scaling. Using a large dataset of Japanese firms that tracked approximately one million firms annually for two decades (1994–2015), we examined up to the trivariate relations between corporate size measures: annual sales, capital stock, total assets, and numbers of employees and trading partners. The data were examined using a multivariate generalization of a previously proposed method for analyzing bivariate scalings. We found that relations between measures other than the capital stock are marked by allometric scaling relations. Power–law exponents for scalings and distributions of multiple firm size measures were mostly robust throughout the years but had fluctuations that appeared to correlate with national economic conditions. We established theoretical relations between the exponents. We expect these results to allow direct estimation of the effects of using alternative size measures of business firms in regression analyses, to facilitate the modeling of firms, and to enhance the current theoretical understanding of complex systems. [ABSTRACT FROM AUTHOR]