The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available.
Statistical Methods for Establishing Personalized Treatment Rules in Oncology / Ma, Junsheng; Hobbs, Brian P.; Stingo, Francesco C. - In: BIOMED RESEARCH INTERNATIONAL. - ISSN 2314-6133. - ELETTRONICO. - 2015:(2015), pp. 1-13. [10.1155/2015/670691]
Statistical Methods for Establishing Personalized Treatment Rules in Oncology
STINGO, FRANCESCO CLAUDIO
2015
Abstract
The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available.File | Dimensione | Formato | |
---|---|---|---|
670691.pdf
accesso aperto
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Open Access
Dimensione
1.44 MB
Formato
Adobe PDF
|
1.44 MB | Adobe PDF |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.