Response to Comment on 'Empirical assessment of methods for risk identification in healthcare data'

摘要

The secondary use of observational healthcare data has the potential to support the characterization of causal associations between medical product exposures and subsequent health outcomes of interest. These data are often used in pharmacoepidemiology studies to estimate the strength of temporal association as an average treatment effect. The method used within a pharmacoepidemiology study can be considered a 'measurement device'. As with any measurement device, it is critical to first understand its operating characteristics (how well it works and whether it is properly calibrated for the objective at hand) before deploying it. This understanding should be considered a prerequisite in identifying whether the data and methods are used to study prespecified hypotheses concerning a single drug-outcome pair or if more systematically applied to multiple drug-outcome pairs as in the proactive identification of potential associations. In this regard, we see the analytical challenge of 'signal generation' and 'signal refinement' as the same and the need to establish operating characteristics as highly applicable to both use cases that Gagne and Schneeweiss articulated [1].