BACKGROUND: The comparison of outcomes from different cohorts in observational studies is subject to many biases, due in part to important imbalances between the characteristics of the cohorts. Many statistical methods exist to control confounding in this context, such as propensity score matching (PSM), inverse probability weighting (IPW), regression (R), and exact matching (EM). Most papers comparing methods either explain the statistical mechanism of each method or perform simulations. In real-word studies, most assumptions cannot be verified. Therefore, there is a need to provide clear guidance on planning the study design scenario for which each method would be optimal, not only from an analytic perspective but from a feasibility approach that considers the essential elements of the underlying research question.

OBJECTIVES: The authors present several popular methods to account for confounding variables in the context of a prospective comparative study and illustrate the pros and cons of those methods applied in typical prospective study scenarios.

METHODS: Several aspects were considered for the conceptualization of the study, such as data sources, data quality, data availability, overlap, and recruitment logistics. Statistical methods presented will focus on PSM, IPW, R, and EM. The methods will be investigated for external control arms, rare diseases, and prevalent disease studies.

RESULTS: We illustrated through a short simulation [N=is 100, 1 binary treatment assignment, 6 other baseline covariates, treatment choice associated with 3 of the baseline covariates], that matching methods were not always preferred. Using PSM, p value=0.146, and with a doubly-robust IPW approach p-value 0.021. The statistical method choice is often influenced by many factors that are outside of the statistical considerations.

CONCLUSIONS: There is no single statistical method that will work as a best strategy for addressing confounding in all prospective observational studies, and methods are influenced by the design, data quality and study feasibility.