Evidence map›Paper›PMID 42410281›Full record

ReviewPharmacoEconomics2026

SMART Planning of Early-Stage Health Economic Decision Models for Mechanism-Based Precision Treatments in Rare Cancers: An Application and Tool Update.

Teebah Abu-Zahra, Sabine E Grimm, Prashant Changoer, Nicolas U Gerber, Stephanie Mathes, Romana T Netea-Maier, Manuela Joore

Abstract readReview
PubMed Publisher
In one paragraph

Review in PharmacoEconomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Teebah Abu-ZahraDepartment of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands. Teebah.abu-zahra@mumc.nl.ORCID http://orcid.org/0000-0002-1090-1721
Sabine E GrimmDepartment of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands.
Prashant ChangoerDivision of Endocrinology, Department of Internal Medicine, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Nicolas U GerberDepartment of Oncology and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.
Stephanie MathesDepartment of Oncology and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.
Romana T Netea-MaierDivision of Endocrinology, Department of Internal Medicine, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Manuela JooreDepartment of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands.

Funding

European Health and Digital Executive Agency 01057619
6 · The paper itself

Abstract

The Systematic Model adequacy Assessment and Reporting Tool (SMART) was developed to help ensure that health decision-analytic models (DAMs) are fit for purpose, by listing key model features for which ideal modelling choices are identified. Then the user is required to report and justify any deviations from the ideal, and the consequences of these deviations for model validity and transparency. SMART can be particularly valuable in early development settings, where limited data and resources often require model simplifications. This study illustrates the application of SMART in developing model plans in such early-stage settings and examines whether updates to the tool are needed. The planned DAMs are for evaluating precision and combination treatments in two rare cancers: advanced thyroid cancer and diffuse midline glioma. Using health economic guidelines, literature, and clinical and pre-clinical expert co-author input, SMART was completed and the models were conceptualized. Deviations from ideal modelling choices included assuming homogeneous populations, adopting narrower model perspectives, omitting diagnostic pathways, and relying on simplifying assumptions. SMART highlighted these deviations, indicating a low-to-moderate impact on model validity and variable impact on transparency. Updates to SMART included refining the decision-context section, adding guiding questions and additional space for supporting resources, expanding instructions on justifying deviations and assessing their consequences, and adding a report-generating function. Using SMART enabled a systematic definition of the decision context and supported consensus between model analysts and clinical and pre-clinical experts on clear, well-justified model plans that are adequate for this resource-constrained development stage.

Identifiers

What Socratic holds

Textmetadata
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.