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01.12.2019 | Research Article | Ausgabe 1/2019 Open Access

BMC Medical Research Methodology 1/2019

A Bayesian non-inferiority approach using experts’ margin elicitation – application to the monitoring of safety events

BMC Medical Research Methodology > Ausgabe 1/2019
Camille Aupiais, Corinne Alberti, Thomas Schmitz, Olivier Baud, Moreno Ursino, Sarah Zohar
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Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12874-019-0826-5) contains supplementary material, which is available to authorized users.
Moreno Ursino and Sarah Zohar are equal contributors.
Moreno Ursino and Sarah Zohar contributed equally to this work.

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When conducing Phase-III trial, regulatory agencies and investigators might want to get reliable information about rare but serious safety outcomes during the trial. Bayesian non-inferiority approaches have been developed, but commonly utilize historical placebo-controlled data to define the margin, depend on a single final analysis, and no recommendation is provided to define the prespecified decision threshold. In this study, we propose a non-inferiority Bayesian approach for sequential monitoring of rare dichotomous safety events incorporating experts’ opinions on margins.


A Bayesian decision criterion was constructed to monitor four safety events during a non-inferiority trial conducted on pregnant women at risk for premature delivery. Based on experts’ elicitation, margins were built using mixtures of beta distributions that preserve experts’ variability. Non-informative and informative prior distributions and several decision thresholds were evaluated through an extensive sensitivity analysis. The parameters were selected in order to maintain two rates of misclassifications under prespecified rates, that is, trials that wrongly concluded an unacceptable excess in the experimental arm, or otherwise.


The opinions of 44 experts were elicited about each event non-inferiority margins and its relative severity. In the illustrative trial, the maximal misclassification rates were adapted to events’ severity. Using those maximal rates, several priors gave good results and one of them was retained for all events. Each event was associated with a specific decision threshold choice, allowing for the consideration of some differences in their prevalence, margins and severity. Our decision rule has been applied to a simulated dataset.


In settings where evidence is lacking and where some rare but serious safety events have to be monitored during non-inferiority trials, we propose a methodology that avoids an arbitrary margin choice and helps in the decision making at each interim analysis. This decision rule is parametrized to consider the rarity and the relative severity of the events and requires a strong collaboration between physicians and the trial statisticians for the benefit of all. This Bayesian approach could be applied as a complement to the frequentist analysis, so both Data Safety Monitoring Boards and investigators can benefit from such an approach.
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