Dr. Pierre-Jerome Bergeron
Department of Statistics and Actuarial Science
University of Waterloo
Title: "Studying the natural history of diseases through prevalent cases: can we exploit untapped features of length-biased data?"
In standard linear regression, though one samples from the joint distribution of the variable
of interest and covariates, the analysis is carried out conditionally because the marginal
distribution of the covariates is considered ancillary to the parameters of interest. When
sampling is done with length-bias with respect to the response variable, as can be the case
with survival data from prevalent cohorts, the covariates are also sampled with a bias.
The question is whether the marginal distribution holds any information about the parameters
and, if so, should one adapt the usual methods of analysis to account for it? We present an
adjusted (joint) likelihood approach for length-biased survival data with left truncation
and right censoring and compare it with a conditional approach which ignores the information
in the sampling distribution of the covariates. It is shown that taking the covariates into
consideration yields more efficient estimates. The methods are applied to data on survival
with dementia from the Canadian Study on Health and Aging (CSHA). If time permits, extension
of these ideas to data on recurrent events will be addressed.