08:30-17:30 classroom teaching

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.

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;