Two assumptions of the prior event rate ratio approach for controlling confounding can be evaluated by self-controlled case series and dynamic random intercept modeling.

ABSTRACT:

Objectives

The prior event rate ratio (PERR) is a recently developed approach for controlling confounding by measured and unmeasured covariates in real-world evidence research and observational studies. Despite its rising popularity in studies of safety and effectiveness of biopharmaceutical products, there is no guidance on how to empirically evaluate its model assumptions. We propose two methods to evaluate two of the assumptions required by the PERR, specifically, the assumptions that occurrence of outcome events does not alter the likelihood of receiving treatment, and that earlier event rate does not affect later event rate.

Study design and setting

We propose using self-controlled case series (SCCS) and dynamic random intercept modeling (DRIM), respectively, to evaluat

SUBMITTER: Cheung YB 

PROVIDER: S-EPMC11636649 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature