Timetable

Monday-Friday
08:30-17:30 classroom teaching

Saturday
09:00-11:30 practical session
11:30-12:00 joint presentation ceremony for all classes at the school
12:00-13:00 lunch

All teaching (lectures and practical exercise sessions) will be in the same room and the exact timetable will vary from day to day. There will be a greater focus on lectures during the first day and a greater focus on other activites (exercises and discussion) during the latter days. No new material will be presented on Saturday.

There will be 30-minute coffee breaks each morning and afternoon and a 1-hour lunch break each day from 13:00-14:00.

New in 2016

We are continually improving the course based on our own experiences and participant feedback. We have further developed and improved the lectures and exercises, added new exercises, and restructured the order in which content is presented. The course content has been updated to reflect recent developments in the field and in our own research. For example: In order to devote more time to some topics we must devote less time to others. We will no longer have a lecture on methods for analysing data with missing covariates, but will distribute the lecture notes and retain the exercise. If anyone has a particular interest in this area we are happy to work with them during the exercise sessions.

The course will cover the following topics

  • 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;
  • Estimating patient survival using the actuarial and Kaplan-Meier methods.
  • Testing for differences in survival between groups using the log-rank test;
  • Methods for estimating expected survival (Ederer I, Ederer II, Hakulinen);
  • Impact of (erroneously) including cancer patients in the population mortality file when estimating expected survival.
  • Comparison of methods (Ederer I, Ederer II, Hakulinen, Pohar Perme) for estimating relative/net survival;
  • Obtaining, constructing, and extending population mortality rates for the purpose of estimating expected and relative survival;
  • Interpreting relative/net survival estimates; statistical cure;
  • Age standardisation of relative survival, including model-based standardisation;
  • Cohort, complete, period and hybrid approaches to estimation;
  • Modelling cause-specific mortality using Poisson regression and Cox regression;
  • Regression diagnostics and goodness-of-fit;
  • Assessing the proportional hazards assumption; non-proportional hazards and how to adjust for them;
  • Comparison of the Cox and Poisson regression models (illustrating that they are very similar);
  • Modelling excess mortality (relative survival) using Poisson regression;
  • Flexible parametric models and their application to modelling cause-specific mortality and excess mortality;
  • Cure models for relative survival - estimating and modelling the cure proportion; flexible parametric cure models;
  • Estimation of life expectation and proportion of expected life lost;
  • Partitioning excess mortality;
  • Estimation in the presence of competing risks;
  • Methods for analysing data with missing covariates (lecture notes but no lecture);
  • Estimating the number of avoidable premature deaths;
  • Discussion of what to include in a (cancer registry) report of cancer patient survival (e.g., the relative merits of various approaches for various target audiences);
  • Impact of data quality, completeness, stage migration, screening and lead-time bias;
  • Potential biases in estimates or patient survival;
  • Standardised mortality ratio versus relative survival ratio;