Evidence-based use of prognostic and propensity score analyses in observational studies

Thomas M. Menino Convention & Exhibition Center 

Propensity score analysis is a preferred method for addressing confounding bias in nonrandomized observational studies by balancing the distribution of baseline covariates across exposure groups. However, it requires a positivity assumption and does not always yield optimal bias reduction. An alternative approach, prognostic score analysis, removes confounding bias by balancing baseline prognosis rather than exposure assignment. The relative usefulness of prognostic versus propensity score models remains unclear in evidence-based biostatistical practice. We evaluated differences between prognostic and propensity score analyses using real and simulation-based data. We found that prognostic score estimation yields lower bias with higher coverage probability across different scenarios compared to propensity score methods. This study demonstrates that the choice between prognostic and propensity score analyses depends on the study design, the distributions of the outcome and exposure, the type of exposure, and the presence of missing data on exposure and covariates. We also provide a step-by-step guide for selecting and using these methods in observational studies.

Propensity score

Prognostic score

Inverse-probability treatment weighting

Survey-weighted analysis

Observational study 

Biometrics Section