[Design strategies and analysis techniques for real-world cancer research]

With the development of precision medicine and big data technology, a large amount of medical data continues to accumulate, providing solid support for the application of real world study (RWS) in the field of cancer. Real world data (RWD) originates from clinical practice and can compensate for the limitations of randomized controlled trials, providing key evidence in the development of tumor prevention and control strategies, drug research and development, and medical insurance decisions. This article systematically reviews the design strategies of tumor RWS, including data source selection, research directions and research design types. It also delves into data analysis techniques such as data processing, statistical analysis methods, and bias control. In addition, this article summarizes the challenges currently faced by tumor RWS, such as data quality, privacy protection, and heterogeneity processing, and future development directions, such as artificial intelligence driven analysis and global collaboration. This article aims to provide methodological references for researchers and promote the standardized development of real world evidence (RWE).