Prevalent Cohort Studies: Length-Biased Sampling with Right Censoring

Prevalent Cohort Studies: Length-Biased Sampling with Right Censoring

Logistic or other constraint often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to conduct a cross-section prevalent cohort study for which we recruit prevalent cases, that is, subjects who have already experienced the initiating event, say the onset of a disease.

When the interest lies in estimating the lifespan between the initiating event and a terminating event, say death for instance, such subjects may be followed prospectively until the terminating event or loss to follow-up, whichever happens first. It is well that prevalent cases have, on average, longer lifespans. As such, they do not form a random sample from the target population; they comprise a biased sample. If the initiating events are generated from a stationary Poisson process, the so-called stationarity assumption, this bias is called length bias.

Masoud Asgharian will present the basics of nonparametric inference using length-biased right censored failure time data. Masoud will then discuss some recent progress and current challenges. The study is mainly motivated by challenges and questions raised in analysing survival data collected on patients with dementia as part of a nationwide study in Canada, called the Canadian Study of Health and Aging (CSHA). Masoud will use these data throughout the talk to discuss and motivate the methodology and its applications.

Speaker

Masoud Asgharian, McGill University, Canada

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk)