Evidence map›Paper›PMID 32807102›Full record

ArticleBMC medical research methodology2020

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

Junhao Liu, Jo Wick, Renee' H Martin, Caitlyn Meinzer, Dooti Roy, Byron Gajewski

Erratum issuedAbstract read
In one paragraph

Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Junhao LiuDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA.
Jo WickDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA.
Renee' H MartinDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA.
Caitlyn MeinzerDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA.
Dooti RoyBoehringer Ingelheim, Ridgefield, CT, 06877, USA.
Byron GajewskiDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA. bgajewski@kumc.edu.ORCID 0000-0002-0383-6845

Funding

Hyperbaric Oxygen Brain Injury Treatment (HOBIT) Trial - CCCU01NS095926 · NINDS · HENNEPIN HEALTHCARE RESEARCH INSTITUTE · PI Byron J. Gajewski, Frederick Kofi Korley · 2017 to 2026
$12.3M
NINDS NIH HHS U01 NS095926
6 · The paper itself

Abstract

backgroundMonitoring 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.

methodsIn 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.

resultsAny 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.

conclusionsThe 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.

Indexed as

Bayes TheoremHumansRandomized Controlled Trials as TopicBayesian hierarchical methodBlinded and Unblinded safety dataTwo-stage monitoring

Identifiers

PMID32807102
PMCPMC7433072

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.