ArticleJournal of the American Statistical Association2019
Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Unravelling the Association Between Uncertainties in Model-based Economic Analysis and Funding Recommendations of Medicines in Australia.PharmacoEconomics · 2025Article
- A Unified Approach for Outliers and Influential Data Detection - The Value of Information in Retrospect.Stat (International Statistical Institute) · 2022Article
- The challenges of data in future pandemics.Epidemics · 2022Review
- Value of Information Analysis in Models to Inform Health Policy.Annual review of statistics and its application · 2022Article
- Health impacts of changes in travel patterns in Greater Accra Metropolitan Area, Ghana.Environment international · 2021Article
- A data value metric for quantifying information content and utility.Journal of big data · 2021Article
- A guide to value of information methods for prioritising research in health impact modelling.Epidemiologic methods · 2021Article
- Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations.Statistical communications in infectious diseases · 2020Article
- Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations.PharmacoEconomics · 2020Review
- Mendelian Randomization analysis of the causal effect of adiposity on hospital costs.Journal of health economics · 2020Article
- Accumulating evidence in ecology: Once is not enough.Ecology and evolution · 2019Article
- Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake.PLoS medicine · 2018Article
- Evidence Synthesis for Stochastic Epidemic Models.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Registered trials
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.