Evidence map›Paper›PMID 34388954›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022

Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial.

Anna Heath, Mark Strong, David Glynn, Natalia Kunst, Nicky J Welton, Jeremy D Goldhaber-Fiebert

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.Medical decision making : an international journal of the Society for Medical Decision Making · 2023
    Trial
  2. Article
  3. Article
  4. 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
  5. Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    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. Article
  8. Article
  9. 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
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

6 authors.

Anna HeathChild Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0002-7263-4251
Mark StrongSchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.ORCID 0000-0003-1486-8233
David GlynnCentre for Health Economics, University of York, York, UK.ORCID 0000-0002-0989-1984
Natalia KunstHarvard Medical School & Harvard Pilgrim Health Care Institute, Harvard University, Boston, MA.
Nicky J WeltonSchool of Social and Community Medicine, University of Bristol, Bristol, UK.
Jeremy D Goldhaber-FiebertStanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.

Funding

CIHR MYG-151207Department of HealthMedical Research Council G0601721Medical Research Council G0802413Medical Research Council MR/K025643/1
6 · The paper itself

Abstract

The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.

Indexed as

AlgorithmsModels, StatisticalCost-Benefit AnalysisHumansUncertaintyexpected value of sample informationresearch design methodsR tutorialsimulation methodsvalue of information

Identifiers

PMID34388954
PMCPMC8793320

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.