Sarah R Haile

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In the last few years, I have focused on applications of statistical methodology, in both clinical trials and epidemiology. Most recently, I have been working with epidemiologists on projects involving network meta-analysis, mixed models, multiple imputation, and development and validation of clinical prediction models. Prior to that, I designed various small randomized trials, or phase II studies with exact single stage designs (for example, A'Hern 2001), but most of these have not yet been published. Data analysis has covered a wide range of areas, including survival analysis, (generalized) mixed models, classification trees, cross-over trials, analysis and comparison of diagnostic tests, and analysis of dose-response data.


I am primarily interested in methodology for and applications of survival analysis. My thesis deals with inference on competing risks data, where there is more than one failure type. Competing risks occur frequently in medical applications. For example, in breast cancer clinical trials, a number of different events may occur, which may be classified into local-regional events and all other events. My work has developed parametric methods for competing risks data. As cause-specific cumulative incidence functions (similar to the cumulative distribution function, though specific to a particular event type) are by their nature improper, I have sought to develop distributions which are more appropriate for competing risks data. Additionally, current methods of inference make specific assumptions about the nature of the relationship between the failure times of each of types of events. I have also developed a joint modeling method which allows investigators to make inference about the relationship between failure times for different event types. My dissertation was successfully defended on July 22, 2008