2-day course: Survival Analysis for Cancer Registry Personnel

A 2-day pre-conference course in conjunction with the annual meeting of the International Association of Cancer Registries, New Orleans LA, USA.

Dates: June 1-2, 2009

Location: New Orleans, Louisiana, USA

Course fee: USD$300

Instructors (see below for biographies):
Paul Dickman, Karolinska Institutet, Stockholm, Sweden
Paul Lambert, University of Leicester, Leicester, UK

Target audience

Anyone with an interest in applied cancer survival analysis, including registry directors and administrators, epidemiologists, statisticians and data analysts, physicians and oncologists, and public health specialists. We will assume participants are familiar with the operations of central cancer registries and we will discuss how methods for data collection and coding impact survival statistics. We will not assume advanced knowledge in statistical methods (this is not a course targeted at just statisticians) but we will assume participants have a basic knowledge of epidemiological and statistical methods. Some parts of the course, however, will be at a more advanced statistical level.

Course aims

to teach the primary statistical methods for population-based cancer survival analysis;
to discuss recent developments and current controversies in estimation and interpretation of cancer survival statistics;
to provide participants with the software tools (Stata) and practical skills that they may conduct survival analysis of real data.

Topics covered include

What is 'population-based cancer survival analysis' and what makes it special compared to other applications of survival analysis?
Net survival; cause-specific survival; relative survival; relative merits of cause-specific survival and relative survival for population-based cancer registry data;
Interpreting relative survival estimates; statistical cure;
Cohort, complete, period and hybrid approaches to estimation;
Modelling excess mortality (relative survival) using Poisson regression;
Non-proportional hazards and how to adjust for them;
Similarity of Poisson regression and Cox regression;
Modelling excess mortality (relative survival) using flexible parametric models;
cure models for relative survival - estimating and modelling the cure proportion;
Impact of data quality, completeness, stage migration, screening and lead-time bias.

The course will consist of lectures and exercise sessions. Participants will be provided with an extensive set of exercises (together with solutions) on a wide range of topics. At least four instructors (Drs Dickman and Lambert plus two or more teaching assistants) will be present during the exercise sessions in order to provide time for one-on-one or small group discussions.

Introductory/background reading

Dickman PW, Coviello E, Hills M. Estimating and modelling relative survival. Stata Journal (in press)

Dickman PW, Adami HO. Interpreting trends in cancer patient survival. Journal of Internal Medicine 2006;260:103-117.

Dickman PW, Sloggett A, Hills M, Hakulinen T. Regression models for relative survival. Statistics in Medicine 2004; 23:51-64.

About the teachers

Paul Dickman is Associate Professor of Biostatistics and deputy head of department at the the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet. He conducts research in epidemiology and biostatistics with particular focus on cancer epidemiology and register-based epidemiology. Dr Dickman has long been interested in the analysis of cancer patient survival, the topic of his 1997 doctoral thesis where he studied with Professor Timo Hakulinen. His primary interests lie in statistical methods for estimating and modelling relative survival. He has published widely in the field of cancer patient survival, is a coauthor of the Stata strs command for estimating and modelling relative survival, and taught courses in cancer survival analysis in eight different countries. Current research, in collaboration with Paul Lambert and others, focuses on cure models for relative survival and flexible parametric models.

Paul Lambert is a senior lecturer in Medical Statistics in the Department of Health Sciences at the University of Leicester. Over the last few years Paul's main research interest has been in developing methods for modelling relative survival. In particular modelling time-dependent covariate effects, incorporating period analysis in statistical models, and the estimation and modelling of 'cure' in population-based cancer studies. He is particularly keen on the use of flexible parametric survival models for both standard and relative survival. These offer a number of advantages in terms of communication of results, for example quantifying absolute levels of risk as well as relative risk. He has developed software in Stata to fit cure models for relative survival (strsmix and strsnmix) and also flexible parametric models (stpm2). His other interests include the use of Bayesian methods in medical research, evidence synthesis and hierarchical models.

PaulDickman.com