Evidence obtained during real world data analysis: what it is and how we form it

The article is devoted to the pressing issue of using real world data (RWD) to prove effectiveness and safety of medical technologies. The authors consider the advantages and limitations of this approach compared to traditional randomized clinical trials. According to the main provisions of the article, RWD complement the results of clinical trials and make it possible to evaluate the effectiveness of drugs in everyday practice. Key stages of conducting RWD-based research are described such as research design, selection and evaluation of data source quality, analytical methods, ensuring transparency and reproducibility. Modern tools for planning and conducting RWD research are presented, for example, the HARPER protocol template, structured SPACE approach, and SPIFD data assessment tool. The features and limitations of RWD are discussed, including their unstructured nature, omissions, and inconsistency. The importance of observing the principles of transparency, integrity, and minimizing systematic errors when working with RWD is emphasized. There is a growing recognition of RWD by regulatory authorities and a need to develop standardized approaches to obtain it. In conclusion, the authors emphasize that with proper application of the research methodology, RWD can provide valuable information for decision-making in healthcare, complementing traditional clinical trials.