Dokument: A nonparametric proportional risk model to assess a treatment effect in time-to-event data
| Titel: | A nonparametric proportional risk model to assess a treatment effect in time-to-event data | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=67967 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20241212-124341-9 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Ameis, Lucia [Autor] Kuss, Oliver [Autor] Hoyer, Annika [Autor] Möllenhoff, Kathrin [Autor] | |||||||
| Dateien: |
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| Stichwörter: | number needed to treat, time-to-event analysis, treatment effect, hazard ratio, risk | |||||||
| Beschreibung: | Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox’s model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for
calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Ameis, L., Kuß, O., Hoyer, A., & Möllenhoff, K. (2024). A nonparametric proportional risk model to assess a treatment effect in time‐to‐event data. Biometrical Journal, 66(4), Article 2300147. https://doi.org/10.1002/bimj.202300147 | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät Medizinische Fakultät | |||||||
| Dokument erstellt am: | 12.12.2024 | |||||||
| Dateien geändert am: | 12.12.2024 |
