Statistical methods for population-based cancer survival analysis

The course will address the principles, methods, and application of statistical methods to studying the survival of cancer patients using data collected by population-based cancer registries. We cover central concepts, such as how to estimate and model relative survival, as well as recent methodological developments including cure models, flexible parametric models, loss in expectation of life, and estimation in the presence of competing risks. Comparison of alternative methodological approaches (e.g., to estimating and modeling relative/net survival) will be a focus of the course and participants will get the opportunity to apply and contrast a range of methods to real data. A large amount of time will be devoted to exercise sessions where Drs Lambert and Dickman along with 3 other experienced faculty members will be available to work with participants individually or in small groups. The exercise sessions will also provide an opportunity for participants to discuss their own research projects with the faculty (and with each other). We encourage potential participants to read the detailed course description at http://cansurv.net/.

Course leader

Paul Dickman (Karolinska Institutet) & Paul Lambert (University of Leicester and Karolinska Institutet)

Target group

Physicians, clinicians and public health professionals from public and private institutions who are looking for systematic training in the principles of epidemiology and biostatistics, or epidemiology applied to health care planning and evaluation. They will acquire familiarity with epidemiological and biostatistical principles and techniques and with the computational tools needed to solve practical problems.

Students in biostatistics and epidemiology, and researchers both from public and private institutions who wish to increase their familiarity with quantitative methods or to deepen their knowledge of a specific area of interest, so they can more effectively address problems in health research. They will gain knowledge in modern, advanced methods useful for health professionals engaged in clinical practice, research and teaching.

Course aim

The course covers central concepts, such as how to estimate and model relative survival, cure models, flexible parametric models, loss in expectation of life, and estimation in the presence of competing risks.