SORA

Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

Bayesian hierarchical methods in the detection of potentially teratogenic first‐trimester medications

Cavadino, A; Prieto-Merino, D; Morris, JK (2020) Bayesian hierarchical methods in the detection of potentially teratogenic first‐trimester medications. Pharmacoepidemiol Drug Saf, 29 (3). pp. 337-346. ISSN 1099-1557 https://doi.org/10.1002/pds.4948
SGUL Authors: Morris, Joan Katherine

[img]
Preview
PDF Published Version
Available under License Creative Commons Attribution.

Download (707kB) | Preview
[img] Microsoft Word (.docx) Accepted Version
Available under License Creative Commons Attribution.

Download (131kB)

Abstract

Purpose Bayesian hierarchical models (BHMs) have been used to identify adverse drug reactions, allowing information sharing amongst adverse reactions and drugs expected to have similar properties. This study evaluated the use of BHMs in the routine signal detection analyses of potential first‐trimester teratogens, where these models have not previously been applied. Methods Data on 15 058 malformed foetuses exposed to first trimester medications (1995‐2011) from 13 European congenital anomaly (CA) registries were analysed. The proportion of each CA in women taking a specific medication was compared with the proportion of that CA in all other women in the dataset (55 CAs × 523 medications). BHMs were grouped by either medications or CAs or by both simultaneously, and the results compared with analysing each medication‐CA combination separately and adjusting for multiplicity using a double false discovery rate (FDR) procedure. The proportions of “high‐risk” medications (medications which have been shown to carry a moderate to high risk of foetal malformations) identified as potential signals were compared, as well as the total number of potential signals requiring follow up (the effective workload). Results BHMs identified more high‐risk medications than the double FDR method, but the effective workload was larger. A BHM grouping both medications and CAs, for example, identified 23% of high‐risk medications compared with 14% by the double FDR; however, there was an increase from 16 to 71 potential signals requiring follow up. Conclusion For comparable effective workloads, BHMs did not outperform the double FDR, which is comparatively straightforward to implement and is therefore recommended for continued use in teratogenic signal detection analyses.

Item Type: Article
Additional Information: © 2020 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian hierarchical models, EUROmediCAT, birth defects, congenital anomalies, false discovery rate, multiple testing, pharmacoepidemiology, pharmacovigilance, signal detection, 1115 Pharmacology and Pharmaceutical Sciences, 1117 Public Health and Health Services, Pharmacology & Pharmacy
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: Pharmacoepidemiol Drug Saf
ISSN: 1099-1557
Language: eng
Dates:
DateEvent
3 March 2020Published
6 January 2020Published Online
3 December 2019Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
1504916Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
PubMed ID: 31908100
Go to PubMed abstract
URI: http://openaccess.sgul.ac.uk/id/eprint/111482
Publisher's version: https://doi.org/10.1002/pds.4948

Actions (login required)

Edit Item Edit Item