OBJECTIVES: Methods of analyzing real world evidence (RWE) generally provide estimates that provide estimates for parameters of a population of patients, whereas the application of RWE to address decision making for the individual patient is less well established. The objective of this study was to review the literature to identify methods used for patient-level decision making using observational, real-world data sources.

METHODS: A systematic literature review was conducted in MEDLINE and PsychInfo. Eligible studies analyzed prospective or retrospective RWE, reported quantitative results, and described statistical methods applicable to patient-level decision making. Following dual eligibility review, details of the study design, methodology, strengths and weaknesses were extracted, verified by a second reviewer, and summarized quantitatively.

RESULTS: A total of 115 articles met eligibility criteria (52 prospective, 22 retrospective, 37 cross sectional, and 4 multiple method studies). The most common statistical methods used in studies applicable to patient-level decision making included logistic regression (n=52,45.2%), followed by cox regression (n=24,20.9%), and linear regression (n=17,14.8%). Logistic regression was used mostly in prospective observational studies (25/52=48.1%) and in cross sectional studies (17/37=45.9%). Several studies included novel statistical approaches such as machine learning, recursive partitioning, and development of mathematical algorithms to predict patient outcomes. The strengths associated with these models included use of large datasets allowing both model development and validation in independent cohorts; however, few studies included external or cross-validation. Recommendations include validation in a different data source and limitations of variables collected.

CONCLUSIONS: The studies identified in this literature search demonstrate a variety of rigorous statistical methods that can be used to develop patient-level decision-making models, tools and resources from large population-based datasets. Future observational research should consider these approaches for predictive modeling and should include both internal and external validation to ensure decision-making tools are accurate and reliable.