My global research interest is Bayesian biostatistical modeling. Despite these limitations, it is important for clinical and public health practice to have the best possible estimates of disease prevalence and the properties of diagnostic tests, such as the sensitivity and specificity. Methodology for the simultaneous estimation of the prevalence and test properties has been developed, and applied to several different areas, including asthma and parasitology.
Current work includes extending the methodologies to correlated tests, and ROC curve estimation.
These designs are of great interest since many medical experiments take several measurements of each subject over time, and MPCP models can sometimes produce improved estimators of important parameters for these designs. In addition, the models are able to answer clinically important questions not addressable by standard analyses. Various MPCP models have been developed, and applied to studies in neurology, public health, traffic safety, and hypertension prevention. Current work includes optimal design for change-point problems and applications to Alzheimer's disease. Consideration of the optimal number of subjects required is an essential step in the planning of virtually every experiment in medicine.