Visual Pruner: Visually guided cohort selection for observational studies

Observational studies are a widely used and challenging class of studies. A key challenge is selecting a study cohort from the available data, or “pruning” the data, in a way that produces both sufficient balance in pre-treatment covariates and an easily described cohort from which results can be generalized. Even with advanced pruning methods, it is often difficult for researchers to see how the cohort is being selected; consequently, these methods are underutilized in research. Visual Pruner is a free, easy-to-use web application that can improve both the credibility and generalizability of observational studies by letting analysts use updatable visual displays of estimated propensity scores and key baseline covariates to refine inclusion criteria. By helping researchers see how covariate distributions in their data relate to the estimated probabilities of treatment assignment, the app lets researchers make pruning decisions based on pre-treatment covariate patterns that are otherwise hard to discover. The app yields a set of inclusion criteria that can be used in conjunction with further statistical analysis in any statistical software.