Dr. Rebecca Betensky’s current methodological research interests are in the areas of survival data, clinical trials, and biomarker studies, as motivated by problems that arise in studies of neurologic diseases and cancer. Within survival analysis, she is working on methods of inference in the presence of dependent truncation. She has developed methods for efficient subject and endpoint selection in Alzheimer’s disease clinical trials and for overcoming the placebo effect in chronic disease trials. She has recently examined the effects of subject compensation on statistical inference from clinical trials, and the reporting of follow-up time and its various meanings. Dr. Betensky’s current research also includes the development of statistical methods for biomarker identification in the presence of time varying endpoints with censoring and truncation and in the absence of a gold standard. She is extending these methods to handle very high dimensional biomarkers, such as gene expression arrays and imaging studies, for which standard methods break down.

Biomarker studies Alzheimer’s disease Very high dimensional biomarkers studies Statistical Methods for Analysis of Array CGH Data Signal processing for accurate detection of copy number variants in cancer Specialized Programs of Translational Research in Acute Stroke at the Partners