In the last few years, I have focused on applications of statistical methodology, in both clinical trials and epidemiology. Most recently, I have been focused on studies related to the pandemic, mostly Covid-19 seroprevalence in school-aged children, at various points in time, including lifestyle factors and health-related quality of life.
Prior to the pandemic, I was working with epidemiologists on projects involving network meta-analysis, development and validation of clinical prediction models, mixed models, and multiple imputation. Prior to that, I designed various small randomized trials, or phase II studies with exact single stage designs (for example, A'Hern 2001, as in this trial, which I designed, but was no longer at the institution when the results were published), but most of these have not yet been published. Data analysis has covered a wide range of areas, including
In 2025, I was awarded Venia Legendi (Habilitation, which gives the title of Privatdozent:in or PD) by the University of Zurich's Medical Faculty. My Habilitation thesis, entitled SARS-CoV-2 in children and adolescents, describes some of the results of the Ciao Corona study, a large population-based cohort study of children and adolescents in the Canton of Zurich during the COVID-19 pandemic. Specifically, the research presented in these five publications is discussed:
My doctoral dissertation dealt with 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.