Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Azzolina, Danila; Berchialla, Paola; Gregori, Darío; Baldi, Ileana

doi:10.3390/ijerph18041833

Cited by 18 publications

(14 citation statements)

References 83 publications

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“…The second challenge is, therefore, how to combine and weight the beliefs of multiple experts into a single prior for each MFA variable of interest. Expert prior elicitation has been widely applied in medicine to design clinical trials [23], assess the effect of specific treatments for rare disorders [24], and inform health-care decision-making [25]. Prior elicitation has also been used in environmental assessments to forecast future wind energy costs [26] and regional climate change [27].…”

Section: Expert Prior Elicitation For Mfamentioning

confidence: 99%

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Dong¹,

Liao²,

Huan³

et al. 2022

Preprint

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Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 U.S. steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods.The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' prior. A sensible, weakly-informative prior is also adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey (USGS) and the World Steel Association (WSA).The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.

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Section: Expert Prior Elicitation For Mfamentioning

confidence: 99%

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Dong¹,

Liao²,

Huan³

et al. 2022

Preprint

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“…In statistics, the main branch of the prior elicitation research is presented in (O'Hagan et al, 2006;Garthwaite et al, 2005;O'Hagan, 2019). For other relevant reviews, see (Chesley, 1975;Wolfson, 1995;Jenkinson, 2005;EFSA, 2014;Oakley and O'Hagan, 2019;Stefan et al, 2020), and in clinical research (Johnson et al, 2010a;Azzolina et al, 2021).…”

Section: Current Main Lines Of Researchmentioning

confidence: 99%

Prior knowledge elicitation: The past, present, and future

Mikkola¹,

Martin²,

Chandramouli³

et al. 2021

Preprint

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Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. In practice, however, we are still fairly far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models in academia and industry. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem.Why are we not widely using prior elicitation? We analyze the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert. The existing prior elicitation literature is reviewed and categorized in these terms. This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology.

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“…A prior elicitation task (62,94,95) was developed to capture experts' beliefs about the probability distribution for physical activity conditioned on the constructs identified relevant in qualitative evidence (i.e. informative prior), The task asked the experts to share their belief about the probability of physical activity in 30 scenarios.…”

Section: Psychological Capabilitymentioning

confidence: 99%

Informing behaviour change intervention design using systematic review with Bayesian meta-analysis: physical activity in heart failure

Amirova¹,

Taylor

Volkmer

et al. 2021

Preprint

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Systematically identified evidence concerning modifiable and contextual barriers and enablers can guide the choice of theory and inform behaviour change intervention design. The Bayesian approach can enable the comparison of the extent to which a barrier or enabler influences behaviour and helps in concluding on the uncertainty in the evidence. It can be used to inform intervention development with stakeholders' accounts using qualitative research. The Bayesian approach facilitates the synthesis of qualitative and quantitative evidence. This review evaluates the evidence on the determinants of physical activity among individuals living with heart failure (HF). In HF, physical activity is associated with improved quality of life, reduced hospitalisation, and increased longevity and is a key component of treatment. Embracing the Bayesian approach in health psychology research, we aimed to synthesise evidence regarding barriers and enablers to physical activity in HF in a way that can inform behaviour change intervention development. Qualitative and observational studies investigating any barriers and enablers to physical activity in adults diagnosed with HF were included in a systemic review with Bayesian meta-analysis following the methodology of Dixon-Woods et al. (2002). Evidence from 19 studies (three qualitative; 16 quantitative studies) was synthesised. Qualitative evidence was annotated using the Theoretical Domains Framework to identify determinants of physical activity participation and represented as a prior distribution using an expert elicitation task. The maximum a posteriori probability (MAP) was calculated as a summary statistic for the probability distribution of physical activity conditioned on each determinant, according to qualitative evidence alone (i.e., prior) and qualitative and quantitative evidence combined (i.e., posterior). The uncertainty in the evidence was expressed using Credible Intervals (CrI). Evidence concerning the modifiable barriers and enablers is highly uncertain: social support(MAP=0.11,CrI:[0.08;0.13]),negative attitude(MAP=0.22,CrI:[0.17;0.27]),positive attitude(MAP=0.27,CrI:[0.23;0.31]),self-efficacy(MAP=0.31,CrI:[0.29;0.33]),symptom distress(MAP=021,CrI:[0.18;0.24]). The contextual barriers are age(MAP=0.22,CrI:[0.22;0.23]), low Left Ventricular Ejection Fraction(MAP=0.20,CrI:[0.19;0.22]), and depression(MAP=0.14,CrI:[0.12;0.16]), low, moderate, and high uncertainty in the evidence, respectively. This work extends the limited research on the modifiable barriers and enablers for physical activity participation by individuals living with HF. The review supports the usefulness of the Bayesian approach in informing intervention development and identifies determinants to physical activity that warrant consideration when designing interventions.

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Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Cited by 18 publications

References 83 publications

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Prior knowledge elicitation: The past, present, and future

Informing behaviour change intervention design using systematic review with Bayesian meta-analysis: physical activity in heart failure

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