PMID- 34260043
OWN - NLM
STAT- MEDLINE
DCOM- 20220420
LR  - 20220531
IS  - 1179-1942 (Electronic)
IS  - 0114-5916 (Print)
IS  - 0114-5916 (Linking)
VI  - 44
IP  - 9
DP  - 2021 Sep
TI  - Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical 
      Trials.
PG  - 949-955
LID - 10.1007/s40264-021-01094-8 [doi]
AB  - 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 clinical trials. 
      METHODS: We used a Bayesian hierarchical model to estimate the site reporting 
      rates and assess the risk of underreporting. We designed the model with Project 
      Data Sphere clinical trial data that are public and anonymized. RESULTS: We built 
      a model that infers the site reporting behavior from patient-level observations 
      and compares them across a study to enable a robust detection of outliers between 
      clinical sites. CONCLUSION: The new model will be integrated into the current 
      dashboard designed for clinical QPLs. This approach reduces the need for on-site 
      audits, shifting focus from source data verification to pre-identified, higher 
      risk areas. It will enhance further QA activities for safety reporting from 
      clinical trials and generate quality evidence during pre-approval inspections.
CI  - (c) 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
FAU - Barmaz, Yves
AU  - Barmaz Y
AUID- ORCID: 0000-0003-1600-9010
AD  - F. Hoffmann-La Roche AG, 4070, Basel, Switzerland.
FAU - Menard, Timothe
AU  - Menard T
AUID- ORCID: 0000-0003-4545-6944
AD  - F. Hoffmann-La Roche AG, 4070, Basel, Switzerland. timothe.menard@roche.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210714
PL  - New Zealand
TA  - Drug Saf
JT  - Drug safety
JID - 9002928
SB  - IM
MH  - Bayes Theorem
MH  - Clinical Trials as Topic
MH  - Humans
MH  - *Machine Learning
MH  - *Patient Safety
PMC - PMC8278191
COIS- Yves Barmaz and Timothe Menard were employed by Roche at the time this research 
      was completed.
EDAT- 2021/07/15 06:00
MHDA- 2022/04/21 06:00
PMCR- 2021/07/14
CRDT- 2021/07/14 12:34
PHST- 2021/06/28 00:00 [accepted]
PHST- 2021/07/15 06:00 [pubmed]
PHST- 2022/04/21 06:00 [medline]
PHST- 2021/07/14 12:34 [entrez]
PHST- 2021/07/14 00:00 [pmc-release]
AID - 10.1007/s40264-021-01094-8 [pii]
AID - 1094 [pii]
AID - 10.1007/s40264-021-01094-8 [doi]
PST - ppublish
SO  - Drug Saf. 2021 Sep;44(9):949-955. doi: 10.1007/s40264-021-01094-8. Epub 2021 Jul 
      14.