Estimating treatment effects using parametric models as counter-factual evidence
Randomisation controlled trial are the gold standard for causal inference, however the rapidly increasing development of new treatments and the movement towards personalised medicine mean there is a need to measure efficacy outside of the costly and time-consuming RCT. Here we propose a method of es...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
Apr 9, 2025
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| In: |
BMC medical research methodology
Year: 2025, Jahrgang: 25, Heft: 1, Pages: 1-11 |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-025-02540-2 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s12874-025-02540-2 Verlag, lizenzpflichtig, Volltext: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-025-02540-2 
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| Verfasserangaben: | Richard Jackson, Philip Johnson, Sarah Berhane, Ruwanthi Kolamunnage-Dona, David Hughes, Susanna Dodd, John Neoptolemos, Daniel Palmer and Trevor Cox |
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| 520 | |a Randomisation controlled trial are the gold standard for causal inference, however the rapidly increasing development of new treatments and the movement towards personalised medicine mean there is a need to measure efficacy outside of the costly and time-consuming RCT. Here we propose a method of estimating treatment effects using parametric models to act as control against which to compare data from an experimental arm. This allows for treatment effects to be estimated where data are only available from an experimental arm and can be a tool useful in the analysis of observational cohorts or for the design and analysis of RCTs. | ||
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