Statistical approaches in ambispective cohort studies: Challenges and methodological insights

Area of Science:

• Epidemiology
• Biostatistics
• Real-World Evidence Research

Background:

• Ambispective cohort studies integrate retrospective and prospective data collection.
• This hybrid design is valuable for understanding long-term treatment outcomes in real-world settings.
• However, these studies face methodological challenges like bias and missing data.

Purpose of the Study:

• To outline key statistical considerations for designing and analyzing ambispective studies.
• To review methods for addressing challenges inherent in ambispective designs.
• To emphasize the importance of robust statistical planning for generating reliable real-world evidence.

Main Methods:

• A structured literature review was performed across PubMed, Scopus, and Web of Science.
• Studies were selected based on relevance to real-world data/evidence and statistical approaches to overcome design challenges.
• Thematic synthesis of extracted data on study objectives and statistical methods was conducted.

Main Results:

• Selection bias can be addressed using propensity score matching and inverse probability weighting.
• Missing data are managed through multiple imputation techniques.
• Time-dependent variables and confounders are analyzed using Cox models, marginal structural models, and mixed-effects models, with joint modeling and Bayesian frameworks enhancing causal inference.

Conclusions:

• Ambispective studies, when properly designed and analyzed, provide a powerful framework for generating reliable real-world insights.
• Addressing methodological challenges through appropriate statistical methods is crucial for valid results.
• Interdisciplinary collaboration and methodological rigor are essential for informing clinical and policy decisions.