ArticlePharmacoEconomics2025
Can We Make Health Economic Decision Models as Simple as Possible, But Not Simpler? Introducing SMART tool.
Article in PharmacoEconomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- SMART Planning of Early-Stage Health Economic Decision Models for Mechanism-Based Precision Treatments in Rare Cancers: An Application and Tool Update.PharmacoEconomics · 2026Review
- An Early Value Assessment of Biomarker-Guided Precision Medicine for Treatment-Resistant Hypertension: A Headroom and Threshold Analysis.Applied health economics and health policy · 2026Article
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
backgroundDeveloping health economic decision-analytic models requires making modelling choices to simplify reality while addressing the decision context. Finding the right balance between a decision-analytic model's simplicity and its adequacy is important but can be challenging.
objectiveWe aimed to develop a tool that supports the systematic reporting and justification of modelling choices in a decision-analytic model, ensuring it is adequate and only as complex as necessary for addressing the decision context.
methodsWe identified decision-analytic model features from the key literature and our expertise. For each feature, we defined both simple and complex modelling choices that could be selected, and the consequences of simplifying a feature contrary to requirements of the decision context. Next, we designed the tool and assessed its clarity and completeness through interviews and expert workshops. To ensure consistency of use, we developed a glossary sheet and applied the tool in an illustrative case: a decision-analytic model on a repurposed drug for treatment-resistant hypertension.
resultsWe conducted five interviews and two workshops with 18 decision-analytic model experts. The developed SMART (Systematic Model adequacy Assessment and Reporting Tool) consists of a framework of 28 model features, allowing users to select modelling choices per feature, then assessing the consequences of their choices for validity and transparency. SMART also includes a glossary sheet. The treatment resistant hypertension case example is provided separately.
conclusionsSMART supports decision-analytic model development and assessment, by promoting clear reporting and justification of modelling choices, and highlighting their consequences for model validity and transparency. Thoughtful and well-justified modelling choices can help optimise the use of resources and time for model development, while ensuring the model is adequate to support decision making.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.