Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.

ABSTRACT:

Background

Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study's conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome.

Methods

We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key pro

SUBMITTER: Kawabata E 

PROVIDER: S-EPMC10158211 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature