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Title: Statistical Methods for Clinical Trials to Support Personalized Medicine: Application to Heterogeneity in Patient Population


Personalized medicine allows the extensive use of information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease. The objectives of personalized medicine include:

  • Predict individual susceptibility to disease based on genetic and other factors;
  • Provide more useful and individualized approaches for preventing disease, based on knowledge of individual susceptibility;
  • Detect the onset of disease at the earliest moments, based on identified biomarkers that arise from changes at the molecular level;
  • Pre-empt the progression of disease, as a result of early detection; and
  • Select and optimize medicines and dosages more precisely and safely to each patient.

In this context, heterogeneity in patient population with respect to clinical and histological characteristics, patient context, and genes that are over or under-expressed, must be taken into account. The most common hereditary cancers include breast cancer, ovarian cancer, prostate cancer and colorectal cancer. For example, BRCA1 and BRCA2 are human genes that belong to a class of genes known as tumor suppressors. Mutation of these genes has been linked to hereditary breast and ovarian cancer. To address heterogeneity among patients, we need better annotation of phenotype data, shared representation of patient characteristics, as well as precise and formal description of patient information stored in clinical data-warehouse along with sophisticated methods to validate biomarkers to be used in clinical trial design and take the whole spectrum of data characteristics in study design and patient care.

Statistical methods for clinical trials will be applied to the Clinical Data Warehouse in Hôpital Européen Georges Pompidou (HEGP), Paris.

Dr. Katsahian is currently a visiting associate professor at the Department of Statistics, George Mason University.