7th – 11th July 2014, Waterloo, Canada
This July sees Brian Tom, Programme Leader within the Methods for the Analysis of Complex Observational and Longitudinal Data (COLD) research theme at MRC Biostatistics Unit, visiting the Department of Statistics and Actuarial Science at the University of Waterloo, Canada, as external examiner for a PhD thesis and invited Department Seminar’s speaker.
“The Department of Statistics and Actuarial Science at Waterloo is among the top academic units for statistical and actuarial science in the world and is home to more than 40 research active full-time faculty working in diverse and exciting areas. The Department is also home to over 900 undergraduate students and about 150 graduate students in programs including Actuarial Science, Biostatistics, Quantitative Finance, Statistics, and Statistics-Computing.” (Information taken from their website)
Seminar: “Approaches for parametric time-to-event analysis with informative entry times: Application to HCV study of time to cirrhosis from infection”. (Brian Tom)
In this talk, we examine maximum and pseudo score approaches for the analysis of prevalence data arising from a referral cohort where entry into the cohort is dependent on a subject’s residual fraction of time remaining to the event of interest, and inference on the incident population is required. Such data are believed to occur in hepatitis C virus (HCV) studies conducted in tertiary care settings, where HCV patients are more likely to be referred to specialist clinics at later stages of disease. The conventional truncation likelihood approach which simply conditions on the time of entry into the cohort does not work here as the referral time and time to the event are correlated. The ignoring of this referral bias has led to higher rates of progression to cirrhosis being reported in studies in specialist clinics compared to those in community-based settings. As cirrhosis linked to HCV infection is a major epidemic of the 21st century, it is important to get an accurate picture of the present and future disease burden facing affected regions in order to inform public health decisions and actions.
Brian Tom is also an affiliated lecturer in the University of Cambridge Statistical Laboratory where he jointly teaches the Applied Statistics course on the Master of Mathematics/Master of Advanced Study (Part III of the Mathematical Tripos). He has previously lectured on and been an examiner for the M.Phil in Epidemiology at the Cambridge Institute of Public Health.