Optimal decision rules for biomarker-based subgroup selection for a targeted therapy in oncology
Throughout recent years, there has been a rapidly increasing interest regarding the evaluation of so-called targeted therapies. These therapies are assumed to show a greater benefit in a pre-specified subgroup of patients - commonly identified by a predictive biomarker - as compared to the total pat...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
7 May 2015
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| In: |
International journal of molecular sciences
Year: 2015, Jahrgang: 16, Heft: 5, Pages: 10354-10375 |
| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms160510354 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.3390/ijms160510354 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/1422-0067/16/5/10354 
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| Verfasserangaben: | Johannes Krisam and Meinhard Kieser |
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| 520 | |a Throughout recent years, there has been a rapidly increasing interest regarding the evaluation of so-called targeted therapies. These therapies are assumed to show a greater benefit in a pre-specified subgroup of patients - commonly identified by a predictive biomarker - as compared to the total patient population of interest. This situation has led to the necessity to develop biostatistical methods allowing an efficient evaluation of such treatments. Among others, adaptive enrichment designs have been proposed as a solution. These designs allow the selection of the most promising patient population based on an efficacy analysis at interim and restricting recruitment to these patients afterwards. | ||
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