Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2020

Open Access 01-12-2020 | Research article

Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials

Authors: Junhao Liu, Jo Wick, Renee’ H. Martin, Caitlyn Meinzer, Dooti Roy, Byron Gajewski

Published in: BMC Medical Research Methodology | Issue 1/2020

Login to get access

Abstract

Background

Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis.

Methods

In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data.

Results

Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios.

Conclusions

The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.
Appendix
Available only for authorised users
Literature
1.
Gould AL, Wang WB. Monitoring potential adverse event rate differences using data from blinded trials: the canary in the coal mine. Stat Med. 2017;36(1):92–104.CrossRef
2.
Wang W, Whalen E, Munsaka M, Li J, Fries M, Kracht K, Sanchez-Kam M, Singh K, Zhou K. On quantitative methods for clinical safety monitoring in drug development. Stat Biopharm Res. 2018;10(2):85–97.CrossRef
3.
Meinert CL. ClinicalTrials: design, conduct and analysis (Vol. 39). New York: OUP USA; 2012.
4.
Meinert CL. Clinical trials dictionary: terminology and usage recommendations. Hoboken: Wiley; 2012.
5.
Fleming TR, Ellenberg S, DeMets DL. Monitoring clinical trials: issues and controversies regarding confidentiality. Stat Med. 2002;21(19):2843–51.CrossRef
6.
Ellenberg SS, Fleming TR, DeMets DL. Data monitoring committees in clinical trials: A practical perspective. Stat Med. 2004;23(10):1661–2.CrossRef
7.
Herson J. Data and safety monitoring committees in clinical trials. Boca Raton: CRC Press; 2016.
8.
European Medicines Agency. Reflection paper on risk based quality management in clinical trials. Compliance Insp. 2013;44:1–15.
9.
US Department of Health and Human Services. FDA Guidance for Industry and Investigators Safety Reporting Requirements for INDs and BA/BE Studies; 2017. p. 2010.
10.
O'Neill RT. Regulatory perspectives on data monitoring. Stat Med. 2002;21(19):2831–42.CrossRef
11.
US Food and Drug Administration. Safety assessment for IND safety reporting: guidance for industry. Silver Spring: FDA; 2015.
12.
Gould AL. Statistical methods for evaluating safety in medical product development. Hoboken: Wiley; 2015.
13.
Chen W, Zhao N, Qin G, Chen J. A Bayesian group sequential approach to safety signal detection. J Biopharm Stat. 2013;23(1):213–30.CrossRef
14.
DuMouchel W. Multivariate Bayesian logistic regression for analysis of clinical study safety issues. Stat Sci. 2012;27(3):319–39.CrossRef
15.
Ball G. Continuous safety monitoring for randomized controlled clinical trials with blinded treatment information: part 4: one method. Contemp Clin Trials. 2011;32:S11–7.CrossRef
16.
Gelman A, Hill J, Yajima M. Why we (usually) don't have to worry about multiple comparisons. J Res Educ Effectiveness. 2012;5(2):189–211.CrossRef
17.
Berry SM, Berry DA. Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biometrics. 2004;60(2):418–26.CrossRef
18.
Amy Xia H, Ma H, Carlin BP. Bayesian hierarchical modeling for detecting safety signals in clinical trials. J Biopharm Stat. 2011;21(5):1006–29.CrossRef
19.
Gelman A, Pardoe I. Bayesian measures of explained variance and pooling in multilevel (hierarchical) models. Technometrics. 2006;48(2):241–51.CrossRef
20.
Wen S, Ball G, Dey J. Bayesian monitoring of safety signals in blinded clinical trial data. Ann Public Health Res. 2015;2(2):1019–22.
21.
Berry SM, Carlin BP, Lee JJ, Muller P. Bayesian adaptive methods for clinical trials. Boca Raton: CRC press; 2010.
22.
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. Boca Raton: CRC press; 2013.
23.
Gelman A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 2006;1(3):515–34.CrossRef
24.
Gajewski BJ, Meinzer C, Berry SM, Rockswold GL, Barsan WG, Korley FK, Martin RH. Bayesian hierarchical EMAX model for dose-response in early phase efficacy clinical trials. Stat Med. 2019;38(17):3123–38.CrossRef
25.
Gajewski BJ, Berry SM, Barsan WG, Silbergleit R, Meurer WJ, Martin R, Rockswold GL. Hyperbaric oxygen brain injury treatment (HOBIT) trial: a multifactor design with response adaptive randomization and longitudinal modeling. Pharm Stat. 2016;15(5):396–404.CrossRef
26.
Mehrotra DV, Heyse JF. Use of the false discovery rate for evaluating clinical safety data. Stat Methods Med Res. 2004;13(3):227–38.CrossRef
27.
Lin LA, Zhan Y, Li H, Yuan SS, Ball G, Wang W. Bridging blinded and unblinded analysis for ongoing safety monitoring and evaluation. Contemp Clin Trials. 2019;83:81–7.CrossRef
28.
Schnell PM, Ball G. A bayesian exposure-time method for clinical trial safety monitoring with blinded data. Ther Innov Regul Sci. 2016;50(6):833–8.CrossRef
29.
Mukhopadhyay S, Waterhouse B, Hartford A. Bayesian detection of potential risk using inference on blinded safety data. Pharm Stat. 2018;17(6):823–34.CrossRef
Metadata
Title
Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials
Authors
Junhao Liu
Jo Wick
Renee’ H. Martin
Caitlyn Meinzer
Dooti Roy
Byron Gajewski
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2020
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-020-01097-6

Other articles of this Issue 1/2020

BMC Medical Research Methodology 1/2020 Go to the issue

Research article

Engaging with Indigenous Australian communities for a human papilloma virus and oropharyngeal cancer project; use of the CONSIDER statement

Research article

Searching for Programme theories for a realist evaluation: a case study comparing an academic database search and a simple Google search

Research article

Informing retention in longitudinal cohort studies through a social marketing lens: Raine Study Generation 2 participants’ perspectives on benefits and barriers to participation

Research article

Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches

Research article

Group penalized generalized estimating equation for correlated event-related potentials and biomarker selection

Research article

Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19