## Course summary: Statistical methods for population-based studies of cancer patient survival

This day will consist primarily of a series of lectures introducing key concepts in population-based studies of cancer patient survival. That is, methods for estimating cancer patient survival using registry data. Heavy focus will be placed on the estimation and modelling of net survival.

Net survival is the most commonly-used measure of patient survival estimated from registry data, yet it is rarely used in other settings (not even cancer clinical trials). We will define net survival, describe why it is the measure of choice, and discuss its interpretation. We will also describe alternative measure and their relative merits.

• What is ’population-based cancer survival analysis’ and what makes it special compared to other applications of survival analysis?

• The role of patient survival in cancer control;

• Net survival; cause-specific survival; relative survival;

• Relative merits of cause-specific survival and relative survival for population-based cancer registry data;

• Comparison of methods (with focus on Ederer II and Pohar Perme) for estimating relative/net survival;

• Interpreting relative/net survival estimates;

• Age standardisation of relative/net survival, including model-based standardisation;

• Cohort, complete, period and hybrid approaches to estimation;

• Modelling excess mortality (relative survival) using Poisson regression and flexible parametric models;

• Estimation in the presence of competing risks; crude probability of death;

• Statistical cure; 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;

• Estimating the number of avoidable premature deaths.

There will be some overlap with the previous day. We will introduce flexible parametric models, but not with the same level of detail as day 1. Many of the methods, including cure models and loss in expectation of life, are estimated in the framework of flexible parametric models but will not be covered on day 1 since the methods are primarily used in the field of population-based cancer survival.

Participants with be provided with sample data and exercises with Stata code and worked solutions, but no time will be devoted to hands-on computing until day 3.