Evidence map›Paper›PMID 28735563›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018

A Gaussian Approximation Approach for Value of Information Analysis.

Hawre Jalal, Fernando Alarid-Escudero

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. A Pragmatic Bayesian Adaptive Trial Design Based on the Value of Information: The Value-Driven Adaptive Design.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  2. Article
  3. Article
  4. Meta-Modeling as a Variance-Reduction Technique for Stochastic Model-Based Cost-Effectiveness Analyses.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  5. A Nonparametric Approach for Estimating the Effective Sample Size in Gaussian Approximation of Expected Value of Sample Information.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  6. Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  7. Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  8. Article
  9. Metamodeling for Policy Simulations with Multivariate Outcomes.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  10. Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022
    Article
  11. Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  12. Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  13. Article
  14. Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  15. Value of Information Analysis in Models to Inform Health Policy.Annual review of statistics and its application · 2022
    Article
  16. Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  17. Article
  18. Article
  19. Value of Information Analysis: Are We There Yet?PharmacoEconomics - open · 2021
    Article
  20. Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.Medical decision making : an international journal of the Society for Medical Decision Making · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hawre JalalDepartment of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA (HJ).
Fernando Alarid-EscuderoDepartment of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA (FA-E).

Funding

Comparative Modeling of Effective Policies for Colorectal Cancer ControlU01CA253913 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI RUTTER, CAROLYN M, ZAUBER, ANN GRAHAM · 2020 to 2025
$6.5M
Comparative Modeling of Colorectal Cancer: Informing Health Policies and Prioritizing Future ResearchU01CA199335 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI KUNTZ, KAREN M, LANSDORP-VOGELAAR, IRIS · 2015 to 2019
$5.5M
NCI NIH HHS U01 CA199335NCI NIH HHS U01 CA253913
6 · The paper itself

Abstract

Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.

Indexed as

Decision TheoryAlgorithmsBayes TheoremCost-Benefit AnalysisInformation ScienceUncertaintyexpected value of sample informationmetamodelingprobabilistic sensitivity analysisuncertaintyvalue of information analysis

Identifiers

PMID28735563
PMCPMC8608426

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.