Lifetime Data Analysis
(with applications to medicine and marketing)
Jean-François Dupuy
Institut de Recherche Mathématique de Rennes
France
http://dupuy.perso.math.cnrs.fr/
--Agnès Lagnoux
Institut de Mathématiques de Toulouse
France
http://perso.math.univ-toulouse.fr/lagnoux/
Description of the lectures
Lifetime data arise in a large variety of fields. A non-exhaustive list includes medicine, economy, engineering,
social and behavioral sciences, marketing. For example, a usual application in medicine considers patients
involved in a clinical trial and the objective is to evaluate a therapy based on observed times between beginning
of the treatment and recovery. In marketing, one is often interested in modeling time to “churn” (or attrition) and
identifying factors that explain that some consumers abandon a product or service in favor of a competitor's.
The main complication with lifetime data (also called “survival” data) is that observations can be “censored”,
that is, they are only partially observed. For example, when a clinical trial evaluating a new therapy ends, some
patients are still ill and the real duration until recovery is only known to exceed the observed duration.
This course introduces the statistical techniques that enable analysis and decision-making based on censored
lifetime data. Topics to be covered include censoring, basic probabilistic tools for lifetime data analysis, nonparametric analysis (Kaplan-Meier estimator and log-rank testing), Cox proportional hazards modeling with
fixed covariates (estimation, testing and model validation).
The emphasis of the course will be on methodology.
All the concepts above will be illustrated with real applications coming from medicine and marketing.
Practical works will be realized within the R environment: http://cran.r-project.org/
Bibliography
- Klein, J.P., Moeschberger, M. Survival Analysis: Techniques for Censored and Truncated Data. Springer,
2003.
- Martinussen, T., Scheike, T.H. Dynamic Regression Models for Survival Data. Springer, 2006.
- Therneau, T.M., Grambsch, P.M. Modeling Survival Data: Extending the Cox Model. Springer, 2000.
Short bio of the lecturers
Jean-François Dupuy is Professor of mathematics and statistics at the National Institute of Applied Sciences of
Rennes (INSA - engineering school: http://www.insa-rennes.fr/en.html). JFD obtained a master of biostatistics in
1998 from University Paris 5 and a PhD in statistics at University Paris 5 (2002). From 2003 to 2009, he worked
as associate-professor at University Paul Sabatier of Toulouse, where he obtained his habilitation thesis in
statistics in 2009. Since 2011, he is Professor at INSA. His research interests include lifetime data analysis,
generalized linear models and extreme value analysis.
Agnès Lagnoux is currently assistant professor at Toulouse University 2-Jean Jaurès and researcher at the
Toulouse Mathematic Institute. She obtained a master of applied mathematics in 2003 from University Toulouse
3 and a PhD in probability and statistics at University Toulouse 3 (2006). The topic of her PhD concerned
efficient simulation of rare events. Estimating their probabilities using standard simulation techniques is
computationally inefficient, so special techniques like importance sampling and splitting or restart are being
developed. She completed her skills with a Post-Doc at EDF, the electrical French group, on the estimation of
EDF load curves with EDF. Her research interests include sensitivity analysis and large deviation principles.