Many well-thought-out research studies fail to answer the questions they pose because of a lack of planning for appropriate study design and subsequent analysis.
I’m passionate about providing optimal design and analysis plans for such projects, so that the results of the study clearly answer the questions posed. My research focuses on the design and analysis of correlated data, particularly high-dimensional data obtained from neuroimaging studies such as MRI, functional MRI (fMRI), diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS).
I have been a researcher for more than 22 years and began my work at Cincinnati Children’s in 2001. My research is funded by the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC) and the Department of Defense (DoD).
One of my significant research contributions was the development of the first infant brain template used to make inferences about neuroimaging research (2008). Since then, many researchers have downloaded this template for use in their studies.
In addition to authoring and co-authoring more than 150 peer-reviewed publications and a book chapter, I have presented my work at national meetings.
I’m currently the director of the Data Management and Analysis Center within the Division of Biostatistics and Epidemiology at Cincinnati Children’s. In this role, — where I often work with other biostatisticians, epidemiologists and informaticists — I enjoy helping others, solving problems and making sense of data. My work takes place in a highly collaborative environment; I partner with teams of basic and clinical scientists representing a variety of disciplines that leverage neuroimaging as part of their studies.
Design and analysis of correlated data. This includes developing inference procedures for intraclass correlation, and kappa statistic. Modeling issues associated with high dimensional data that arise from brain imaging studies such as fMRI, MRI and DTI.
Professor, UC Department of Pediatrics