In many branches of science including demography, epidemiology, medicine and engineering,
a considerable amount of information is collected on the nature and timing of events of interest.
In the context of medical research this data may represent the time and
nature of a variety of clinically important health related events occurring
over the course of an individual's life as well as any additional explanatory variables.
In the context of immunologic research, for example, this data may be the dates of infection with the HIV,
diagnosis with AIDS, various opportunistic infections, and death.
In cancer research it may represent the dates of diagnosis with bladder cancer,
of subsequent recurrences, of metasteses, or death.
Finally, in cardiovascular trials, event history data may consist of the dates and types of various types of cardiac events
(i.e. angina attacks, arrythmias, myocardial infarction), strokes (left/right hemisphere, etc.), and thromboses.
Event history analysis is concerned with the modeling this type of data,
typically with a view to one of the following objectives:
i) to accurately reflect aspects of the natural history of the disease,
ii) to identify risk factors for disease progression,
iii) to provide measures of the effect of medical or surgical interventions, or
iv) to provide a basis for prediction about the future course of the disease at the patient or population level.
I collaborate with
for much of this work.
Longitudinal data arise when individuals are assessed repeatedly over time and responses and
explanatory variables of interest are recorded at each assessment.
The most suitable method for analysing data from a particular study depends on the primary scientific question,
but all valid methods must address the serial correlation in the responses over time.
The most common methods are based on random effect models, marginal (population-averaged) models, and transitional models.
My primary interest is in the development of extensions of these methods for
the analysis of longitudinal data which has cross-sectional clustering,
incomplete responses, measurement error, and other challenging features.
The need for efficient use of available resources in medical research has led to
the increased appeal of clinical trial designs based on multiple outcomes.
One of my interests in the recent past has been in the development of methods
that facilitate the design and analysis of randomized trials in which treatment comparisons
are to be made on the basis on multivariate responses.
Issues that require consideration in the area include
the estimate of approximate multivariate distribution and multiple comparisons.
I collaborate with researchers in
rheumatology, transfusion medicine, and public health.
Centre for Prognosis in Rheumatic Diseases,
Toronto Western Hospital, University Health Network
McMaster Centre for Transfusion Research,
School of Public Health and Health Systems,
University of Waterloo
Faculty of Health Sciences,