In the information age, computing capabilities are greatly expanding which has enabled the use of Bayesian methods to a much greater extent. The application of Bayesian methodology to health outcomes is a fairly recent development and addresses problems that have not yet been adequately answered. Four women who have specialized in this area, from a new graduate to those in the field for years will discuss a spectrum of views from across the country. In order to reduce the size and duration of clinical trials, surrogate endpoints, such as laboratory values, are increasingly being used as clinical trial endpoints instead of the clinical events, such as death or worsening clinical condition, that are of true interest. This talk will discuss Bayesian methods for attempting to determine whether the same conclusions are likely to be reached using surrogate endpoints as would have been reached using the true clinical endpoints.
Results have implications for the process by which policy is formed.
There are many statistical innovations in this work, including the application of latent class models to ROC curve estimation, the form of the ROC models used for symptom checklist distributions, and the inclusion of longitudinal assessments within subjects. These models provide more realistic estimates of symptom checklist accuracy than frequentist models that assume an error-free depression diagnosis. The models we develop are generally useful for mental health diagnoses with a 'true state' that is based on a constellation of symptoms. She will summarize, discuss, and identify the common-threads of the applications and the statistical approaches from these three talks.