Visit of Assoc. Dr. Alexander De Leon
Assoc. Dr. Alexander De Leon is expected to visit our department in Fall 2013-2014 semester.
Assoc. Dr. Alexander De Leon is an associate professor in the Department of Mathematics and
Statistics at the University of Calgary, Canada. His research interests include methods for
analyzing correlated data, multivariate models and distances for mixed discrete and continuous
outcomes, pseudo- and composite likelihood methods, copula modeling, assessment of
diagnostic tests, statistical quality control, and statistical problems in medicine.
If funding is approved, he will be teaching a graduate level course titled “Joint Analysis of
Mixed Discrete and Continuous Data: Methods and Applications”. He also kindly accepted to
give three seminars. Abstracts and tentative dates of the seminars are provided below.
Seminar 1
Title: Binocular sensitivity and specificity of diagnostic and screening tests in cross-sectional
studies of paired organs
Tentative Date/Time: 24 October 2013, Thursday, 15:40
Venue: Moti Lal Tiku Meeting Room, Dept. of Statistics
Abstract: Diagnostic and screening studies in ophthalmology frequently involve binocular data
where pairs of eyes are evaluated, through some diagnostic procedure, for the presence of certain
diseases or pathologies. It is usually sufficient in practice that at least one eye is positively
diagnosed for the patient to be sent for further and more extensive eye examination. More
relevant diagnostic accuracy measures in these cases are therefore the probability of at least one
correct positive diagnosis in patients with one or both eyes truly diseased and the probability of
two correct negative diagnoses for patients with both eyes truly undiseased. The former is
analogous to sensitivity and the latter to specificity. Predictive values may be similarly redefined.
The talk proposes new sensitivity and specificity measures as alternatives to conventional ones
for binocular data. The measures are defined for flexible models based on copulas and
extensions of existing models for correlated binary data. The proposed methodology is
illustrated with data from a study on diabetic retinopathy.
Seminar 2
Title: Gaussian copula mixed models for clustered mixed outcomes, with application in
developmental toxicology
Tentative Date/Time: 21 November 2013, Thursday, 15:40
Venue: Moti Lal Tiku Meeting Room, Dept. of Statistics
Abstract: This talk is concerned with the analysis of clustered data from developmental toxicity
studies with mixed responses, i.e., where each member of the cluster has binary and continuous
outcomes. A copula-based random effects model is proposed that accounts for associations
between binary and/or continuous outcomes within clusters, including the intrinsic association
between the mixed outcomes for the same subject. The approach yields regression parameters in
models for both outcomes that are marginally meaningful, and permits the adoption of flexible
marginal distributions for the mixed outcomes as well as for the random effects. The model
includes the correlated probit model of Gueorguieva and Agresti (2001) and the generalized
linear mixed models of Faes et al. (2009) as special cases. Maximum likelihood estimation of
our model parameters is implemented using standard software, such as PROC NLMIXED in
SAS. Results of simulations concerning bias and efficiency of estimates will be reported. The
proposed methodology is motivated by and illustrated using a developmental toxicity study of
ethylene glycol (EG) in mice.
Seminar 3
Title: Classification of multiple mixed outcomes via copulas: Application of pairwise likelihood
methods
Tentative Date/Time: 12 December 2013, Thursday, 15:40
Venue: Moti Lal Tiku Meeting Room, Dept. of Statistics
Abstract: We study the problem of classifying an individual into one of several populations
based on multiple mixed discrete and continuous data. Specifically, we obtain a classification
procedure as an extension to the so-called location linear discriminant function, by specifying a
model for the joint distribution of the mixed discrete and continuous variables using copulas. To
alleviate the computational demands of estimation, a composite likelihood approach based on
pairwise likelihoods is adopted that approximate the full likelihood. Classification rules are then
constructed via the pairwise likelihoods for which we outline methods for estimating
misclassification error rates. Results of simulations on the performance of proposed classification
rules, specifically on their robustness to model misspecification, in various settings are reported.
Four medical data sets comprising mixed binary and continuous variables are used as examples
to illustrate the methodology.