In order to promote the openness, transparency and standardization of clinical trials, improve the scientific and reliability of results, and reserve the manpower, material, and financial resources in the process of clinical trials, this study constructed an integrated intelligent management platform for clinical trials, which could carry out various types of clinical trials such as randomized controlled trials, non-randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies simultaneously. The platform covers the whole process of scheme design, recruitment, follow-up, data analysis, and quality control. This paper mainly introduced the practical needs, design concept, basic framework and technical highlights to provide auxiliary tools for promoting the standardization and intelligence of clinical trials with energy saving and optimal efficiency.
In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.
