To meet, check my online schedule.
My research interests cover several topics: human judgement and decision-making, Bayesian decision theory, behavioral statistics, and the impact of technology on patient decision-making. These areas of inquiry share a common theme---how decisions are actually made and how quantitative methods can be used to describe decision-making and in some instances to improve it.
Patient decision-making often involves a myriad of complicated decision factors such as personal goals and expectations, familial reasons, affective styles, and economic considerations. These factors and many other putative reasons are intertwined and often can not be controlled in a research study. I use multivariate statistical methods to unpack these decision factors and to describe how they interact with one another. The multivariate statistical methods I use include Multivariate ANOVA, Structural Equation Modeling, Hierarchical Linear Modeling, Regression Trees, Cluster Analysis, and Item Response Theory. I use these methods to develop survey instruments to assess patient expectations and goals in breast reconstructive surgery after breast cancer, in genetic testing for cancer risk, and in genetic testing for congenital hearing loss.
Patients' goals, aspirations, and preferences for outcomes are playing increasingly important roles in the uptake of biotechnology, genetic testing, and gene therapy. I design computer-interactive decision aids to help patients and parents to make decision most consistent with existing outcomes data, taking into consideration their goals, values, and preferences for outcomes.
I am both a research psychologist and a behavioral statistician. Thus I use behavioral statistics to study the psychology of decision-making. I am working towards a greater understanding on how the merging of these two fields can empower decision-makers to make better decisions.
NIH Grant-Related Materials
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Linux and Web