Statistical approaches in ambispective cohort studies: Challenges and methodological insights

Abstract Ambispective cohort studies combine retrospective and prospective data collection to provide a comprehensive understanding of treatment outcomes over time. This hybrid design offers several advantages, particularly in real-world settings where long-term patient follow-up is essential. However, integrating historical data with ongoing observations presents methodological challenges, including selection and temporal biases, missing data, confounding variables, and inconsistencies across data sources. This review outlines key statistical considerations for designing and analyzing ambispective studies. Challenges such as selection bias are addressed using methods such as propensity score matching and inverse probability weighting, while missing data are managed through multiple imputation. Time-dependent variables and confounders are tackled using Cox models, marginal structural models, and mixed-effects models. Techniques such as joint modeling, landmark analysis, and Bayesian frameworks help strengthen causal inference and account for temporal heterogeneity. A structured literature review was conducted across PubMed, Scopus, and Web of Science using predefined keywords related to ambispective design and statistical methods. Studies were selected based on relevance to real-world data/real-world evidence and the presence of statistical approaches to overcome design-related challenges. Data were extracted on study objectives and then thematically synthesized. This review highlights the importance of a robust statistical analysis plan, interdisciplinary collaboration, and methodological rigor. When appropriately designed and analyzed, ambispective studies offer a powerful framework for generating reliable, real-world insights to inform clinical and policy decisions.