Bayesian Analysis of Longitudinal Studies: 26-27 November 2015, Stellenbosch, South Africa

Posted on Tue, Aug 11 2015 14:29:00

Prof. Emmanuel Lesaffre of the Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Catholic University of Leuven, Belgium, presented this intensive two-day course at Stellenbosch under the auspices of the South African DST/NRF Centre for Epidemiological Modelling and Analysis (SACEMA). The course took place from 26-27 November 2015 at the Stellenbosch Institute for Advanced Study (STIAS).

 

For full announcement click here

 

Course summary:

Longitudinal studies constitute an important class of studies in clinical research and arise when a response is repeatedly measured over time. This is in contrast to a survival study where the time to an event is recorded. In the first case the statistical analysis involves techniques for repeated measures, while in the second case one essentially uses survival analysis. In the last decade a combination of the two statistical techniques is becoming popular and involves the joint modeling of survival times and longitudinal measurements. Longitudinal data can be analyzed in the classical, frequentist framework, but the Bayesian approach offers more flexible modeling options which could be useful when the data structure is complex (multivariate outcomes, multiple levels in the data, some missing data patterns, joint modeling, etc.). We will illustrate the Bayesian approach for the analysis of such data, by means of a great variety of examples. Examples will be analysed using WinBUGS/OpenBUGS/JAGS and R-versions of them, but also other software will be used. The course consists of 2 parts: Part I: introduction to the Bayesian approach based on the newly released book Bayesian Biostatistics of Lesaffre and Lawson and Part II: devoted to the analysis of FU studies.

 

Recommended reading:

E. Lesaffre and A. Lawson (2012) Bayesian Biostatistics (Statistics in Practice). John Wiley & Sons, New York, ISBN 978-0-470-01823-1.

M.J Daniels and J.W. Hogan (2007) Missing Data in Longitudinal Studies. Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman & Hall/CRC, Boca Raton, ISBN 978-1-58488-609.

 

Prerequisites:

The course will be oriented towards an applied audience with a good knowledge of various regression models. Some advance knowledge of classical repeated measurements analysis and classical survival analysis will be helpful. The Bayesian concepts will be introduced only briefly; hence a prior course on the Bayesian approach will certainly be helpful. The concepts will be explained on real data examples for which either R, WinBUGS, etc. code will be provided together with data to try out at a later time. Knowledge of R will be quite useful for the course, but no prior knowledge of WinBUGS is assumed, although this also would be helpful.


Emmanuel Lesaffre

Emmanuel Lesaffre is Professor of Biostatistics at I-Biostat, K.U.Leuven, Leuven, Belgium. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval censored data, misclassification issues and clinical trials. He has written more than 350 papers in peer-reviewed statistical and medical journals. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA. He (co-)authored five books among which the recently published Wiley book Bayesian Biostatistics (2012) together with Andrew Lawson. He has taught many statistical courses on a variety of topics in regular master programs, but also short courses on-site both at national and international level. The audiences have included medical students and researchers, engineers, mathematicians and statisticians.