Evidence mapPaperPMID 31549359Full record

ArticlePharmacoEconomics2019

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

Fernando Alarid-Escudero, Eline M Krijkamp, Petros Pechlivanoglou, Hawre Jalal, Szu-Yu Zoe Kao, Alan Yang, Eva A Enns

Abstract read
In one paragraph

Article in PharmacoEconomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 2 pooled it
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

37 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  15. 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
  16. 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
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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

7 authors.

Fernando Alarid-EscuderoDrug Policy Program, Center for Research and Teaching in Economics (CIDE)-CONACyT, Circuito Tecnopolo Norte 117, Col. Tecnopolo Pocitos II, 20313, Aguascalientes, AGS, Mexico. fernando.alarid@cide.edu.ORCID 0000-0001-5076-1172
Eline M KrijkampDepartment of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.ORCID 0000-0003-3970-2205
Petros PechlivanoglouThe Hospital for Sick Children and University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-5090-7936
Hawre JalalDepartment of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0002-8224-6834
Szu-Yu Zoe KaoDivision of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA.ORCID 0000-0002-4987-3983
Alan YangThe Hospital for Sick Children, Toronto, Ontario, Canada.ORCID 0000-0002-0344-6812
Eva A EnnsDivision of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA.ORCID 0000-0003-0693-7358

Funding

Institutional Career Development CoreKL2TR001856 · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2025 to 2025
$1.5M
NCATS NIH HHS KL2 TR001856NCI NIH HHS U01 CA199335NCI NIH HHS U01- CA-199335NIAID NIH HHS K25 AI118476NIAID NIH HHS K25AI118476NIH HHS KL2 TR0001856
6 · The paper itself

Abstract

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Indexed as

Decision MakingDecision Support TechniquesSoftwareCost-Benefit AnalysisHumansReproducibility of Results

Identifiers

PMID31549359
PMCPMC6871515

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.