Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

ABSTRACT:

Introduction

Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AEs) underreporting remains a recurrent issue. In a recent project, we developed a predictive model that enables oversight of AE reporting for clinical quality program leads (QPLs). However, there were limitations to using solely a machine learning model.

Objective

Our primary objective was to propose a robust method to compute the probability of AE underreporting that could complement our machine learning model. Our model was developed to enhance patients' safety while reducing the need for on-site and manual QA activities in clini

SUBMITTER: Barmaz Y 

PROVIDER: S-EPMC8278191 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature