Patient subgroup identification for clinical drug development
| Autor: | Arunava Chakravartty, Yan Sun, Viswanath Devanarayan, Paul Trow, Saptarshi Chatterjee, Lu Tian, Xin Huang |
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| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
medicine.medical_specialty Epidemiology business.industry Mechanism (biology) Experimental data Context (language use) Precision medicine 01 natural sciences Clinical trial 010104 statistics & probability 03 medical and health sciences Identification (information) 0302 clinical medicine Drug development 030220 oncology & carcinogenesis Econometrics Medicine Biomarker (medicine) 0101 mathematics business Intensive care medicine |
| Zdroj: | Statistics in Medicine. 36:1414-1428 |
| ISSN: | 0277-6715 |
| Popis: | Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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