Usual time: Thursdays 14:00-15:00
Location: Room 102, Department of Statistical Science, 1-19 Torrington Place (1st floor). Some seminars are held at different locations and at different times. Please click on the abstract for further details.
- 03 May 2018: Dr. Theresa Smith (University of Bath)
Age-period-cohort models for cancer incidence and mortality
Age-period-cohort models have been used to examine and forecast cancer incidence and mortality for over three decades. However, the fitting and interpretation of these models requires great care because of the well-known identifiability problem that exists; given any two of age, period, and cohort, the third is determined. In this talk I introduce APC models and the identifiability problem. I examine proposed ‘’solutions’’ to this problem and approaches based on an identifiable parametrization. I conclude with an analysis of cancer incidence data from Washington State and a discussion of future research directions.
- 10 May 2018: Dr. Tyler Helmuth (University of Bristol)
- 17 May 2018: Dr. Vanda Inacio De Carvalho (University of Edinburgh)
Bayesian nonparametric inference for the covariate-adjusted ROC curve
Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased states must be rigorously assessed through statistical analysis. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the discriminatory ability of continuous-outcome diagnostic tests. It has been acknowledged that several factors (e.g., subject-specific characteristics, such as age and/or gender) can affect the test's accuracy beyond disease status. Recently, the covariate-adjusted ROC curve has been proposed and successfully applied as a global summary measure of diagnostic accuracy that takes covariate information into account. We develop a highly flexible nonparametric model for the covariate-adjusted ROC curve, based on a combination of a B-splines dependent Dirichlet process mixture model and the Bayesian bootstrap, that can respond to unanticipated features of the data (e.g., nonlinearities, skewness, multimodality, and/or excess of variability). Multiple simulation studies demonstrate the ability of our model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. Our methods are motivated by and applied to an endocrine dataset where the main goal is to assess the accuracy of the body mass index, adjusted for age and gender, for predicting clusters of cardiovascular disease risk factors.
- 24 May 2018: Prof. Panos Vassiliou (Aristotle University of Thessaloniki and University College London)
Laws of large numbers in inhomogeneous Markov systems
- 31 May 2018: Dr. José Miguel Hernández Lobato (University of Cambridge)
- 19 July 2018: Prof. Baogang Hu (Chinese Academy of Sciences)