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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]