GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.
Open accessAbstract
The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi-state models, discrete event simulation models, physiology-based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real-world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
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- Authors
- Panayidou, K., Gsteiger, S., Egger, M., Kilcher, G., Carreras, M., Efthimiou, O., Debray, T. P. A., Trelle, S., Hummel, N., & on behalf of the GetReal methods review group
- Affiliations
- University of Bern (CH)
- F. Hoffmann-La Roche AG (CH)
- University of Ioannina (GR)
- University Medical Center Utrecht (NL)
- Corresponding author
- Egger, Matthias
- Venue
- Research Synthesis Methods
- Publisher
- Wiley
- Published
- 2016-08-16
- Volume
- 7
- Issue
- 3
- Pages
- 264-277
- ISSN
- 1759-2879 (ISSN-L)
- 1759-2887
- PMC
- PMC5129568
- OpenAlex
- W2520975329
- Cited by
- 24
- References
- 72
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 3 · 2016: 1
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Research Synthesis Methods (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc-nd)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Open Access CRIS of the University of Bern
- Funders
- Innovative Medicines Initiative Joint Undertaking (IMI JU) (115546)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- real-world effectiveness, mathematical modeling, multi-state models, discrete event simulation, survival models, generalized linear models, individual patient data, medication adherence, external validation, sensitivity analysis, cardiovascular disease, metabolic disorders, neurological disorders
- MeSH terms
- Cardiovascular Diseases, Computer Simulation, Drug Evaluation, Drug Therapy, Pharmaceutical Preparations, Humans, Metabolic Diseases, Models, Theoretical, Nervous System Diseases, Software, Reproducibility of Results, Linear Models, Randomized Controlled Trials as Topic, Databases, Bibliographic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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