Missing data in real-world evidence studies: management strategy from planning to analysis

Missing data is one of the key characteristics of Real-World Data and they are an integral part of the Real-World Evidence studies (RWE studies). They arise at all stages of data collection in both prospective and retrospective designs. This review provides a comprehensive analysis of the issue of missing data in the RWE studies at all stages of the research process – the initial conceptualization of the study to the interpretation of results and their presentation in scientific publications.

The first part of the review examines the theoretical foundations: the term "missing data" in the regulatory documents of ICH, FDA, EMA, EAEU and its slightly different definitions, the role of the data source in the occurrence of missing data, the classification of missing data mechanisms according to Rubin (MCAR, MAR, MNAR) and their significance for selecting statistical methods.

The second part of the review focuses on a systematic approach and outlines eight stages of handling missing data. It is emphasized that effective missing data management begins well before statistical analysis — at the study design stage. Effective missing data management is an end-to-end, proactive process integrated into the RWE study lifecycle.

The conclusion of the report substantiates the need for a systematic approach to the issue of missing data as a crucial element in ensuring the reliability of RWE, which is directly relevant to the application of their findings within the healthcare system.

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