Assessing the prior event rate ratio method via probabilistic bias analysis on a Bayesian network.

ABSTRACT:

Background

Unmeasured confounders are commonplace in observational studies conducted using real-world data. Prior event rate ratio (PERR) adjustment is a technique shown to perform well in addressing such confounding. However, it has been demonstrated that, in some circumstances, the PERR method actually increases rather than decreases bias. In this work, we seek to better understand the robustness of PERR adjustment.

Methods

We begin with a Bayesian network representation of a generalized observational study, which is subject to unmeasured confounding. Previous work evaluating PERR performance used Monte Carlo simulation to calculate joint probabilities of interest within the study population. Here, we instead use a Bayesian networks framework.

Results

Using this str

SUBMITTER: Thommes EW 

PROVIDER: S-EPMC7027899 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature