Meta-analysis of blinded and unblinded studies for ongoing aggregate safety monitoring and evaluation.

Blinded analysis Evidence synthesis Meta analysis Safety monitoring and evaluation Unblinded analysis

Journal

Contemporary clinical trials
ISSN: 1559-2030
Titre abrégé: Contemp Clin Trials
Pays: United States
ID NLM: 101242342

Informations de publication

Date de publication:
08 2020
Historique:
received: 24 02 2020
revised: 02 06 2020
accepted: 11 06 2020
pubmed: 1 7 2020
medline: 25 9 2021
entrez: 1 7 2020
Statut: ppublish

Résumé

During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events.

Identifiants

pubmed: 32603785
pii: S1551-7144(20)30146-4
doi: 10.1016/j.cct.2020.106068
pii:
doi:

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

IM

Pagination

106068

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Li-An Lin (LA)

Clinical Safety Statistics, Merck & Co., Inc., Rahway, NJ, USA. Electronic address: li.an.lin@merck.com.

Shuai Sammy Yuan (SS)

Biometrics Team, Kite Pharma, a Gilead Company, Santa Monica, CA, USA.

Lie Li (L)

Clinical Safety Statistics, Merck & Co., Inc., North Wales, PA, USA.

Greg Ball (G)

Clinical Safety Statistics, Merck & Co., Inc., Rahway, NJ, USA.

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