Statistical Power and Sample Size in Personalized Medicine

Statistical Power and Sample Size in Personalized Medicine

Alexander Rompas, Charalampos Tsirmpas, Athanasios Anastasiou, Dimitra Iliopoulou, Dimitris Koutsouris
Copyright: © 2013 |Pages: 17
DOI: 10.4018/ijsbbt.2013040105
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Abstract

Personalized medicine (PM) is a rapidly growing field of healthcare and medicine. The advantage of a personalized medicine is the availability of each person’s unique genetic and genomic print. The healthcare that incorporates personalized medicine provides coordinated, continuous patient-specific data. The goal of personalized medicine is to promote health wellness, satisfaction, and to increase the likelihood of a successful disease prevention, detection and treatment. This form of medicine, apart from patient’s personal data and medicine-biological measurements, uses genomic information data to understand the molecular structure of the disease and to optimize health care strategies and drug therapies. Clinical trials that investigate personalized approaches are subject to special rules, for example, pertain the selection of participating patients. In personalized medicine, a certain genetic profile must be identified so that the treatment can work. This is why potential participants are first screened and selected accordingly for clinical trials. The special design of such clinical trials has an impact on the evaluation of data collected during the given study.
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Cancer Management

Oncology is a primary section of medicine consisting of various cancer phases, measured in centimeters in most cases, and types with regard to their anatomy and pathology. Cancer genetics is a subgroup falling under the category of oncology that is focusing on genes and is associated with inherited cancer risk (Mansour & Schwarz, 2008). There is a limited number of cancerous disorders in which homogeneity separates in an autosomal dominant fashion, leading to considerably higher risk for certain cancers types. It is considered that inherited cancer genetics factors explain only about 5-10% of all cancers cases. Nevertheless, other genetic modifications with more indirect effects associated to cancer risk may trigger detailed cancer risk valuation to patients who are not associated with a family history (Yan, 2008).

Examples of personalized cancer management:

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