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?"

* Abstract:*

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.