General info

The time will be divided equally between classroom lectures and practical sessions. All faculty will be present at the practical sessions; exercises with worked solutions will be provided but these sessions are also intended as a forum for participants to talk to the faculty about aspects of particular interest to them. We have several group rooms available and can organise informal mini-lectures or group discussions on topics of particular interest.

Questions about the Programme can be sent to

Monday-Tuesday: All participants

Presenters: Paul Lambert, Therese Andersson

Introduction: Revision of concepts and classical models used in survival analysis (including a review of Poisson regression and Cox regression).

Flexible parametric models (FPM): The majority of survival analyses make use of the Cox proportional hazards model, with researchers generally being less keen on using parametric survival models due to the restrictive assumptions about the shape of the underlying hazard/survival functions. Flexible parametric models make use of restricted cubic splines to fit models for survival data. One of the advantages of the models is the ease at which time-dependent effects (e.g. for non-proportional hazards) can be fitted. A further advantage of parametric models is the ability to make various useful predictions, for example time-dependent hazard ratios, differences in hazard functions or differences in survival functions. The aim of this lecture is to provide an understanding of how to fit and interpret flexible parametric models, and to demonstrate a variety of useful predictions from the models, including a variety of ways to quantify differences between groups.

Wednesday-Thursday: Parallel tracks

Track 1: Population-based cancer patient survival analysis (Paul Dickman, Paul Lambert (Wednesday only), Therese Andersson)

Description: This track will focus on application of flexible parametric models to estimate patient survival using data collected by population-based cancer registries. Primary focus will be on estimating and modelling excess mortality (relative survival) but we will also show how to estimate and model cause-specific mortality. We will introduce the competing risks framework and describe how to estimate probabilities of death both in the presence and absence of competing risks. We will also discuss model-based standardisation, flexible parametric cure models, and estimation of life expectation and proportion of expected life lost. The track will commence with an overview of the field and what makes it special compared to other applications of survival analysis. We will describe the measures used in the field (net survival; cause-specific survival; relative survival) and discuss the relative merits of cause-specific survival and relative survival for population-based cancer registry data.


Track 2: Competing risks and multistate models (Sandra Eloranta & Michael Crowther)

Description: Key quantities such as the cause-specific, marginal and subdistribution hazards, as well as their survival analogs (cause-specific cumulative incidence, Kaplan-Meier function etc) will be explained. The focus of the course will be to provide a conceptual understanding of when statistical methods for competing risks are required (given a specific research question), what methodology and software are available and how results are interpreted.
Multi-state models: Multi-state models allow rich insights into complex disease pathways, where a patient may experience many non-fatal/intermediate events, and we wish to the investigate covariate effects for each specific transition between two states, not just for example, on the first event, or a terminal event. This course will introduce the basic concepts of multi-state models, including both Markov and semi-Markov models, and describe the calculation of useful quantities such as transition probabilities. Current methodological approaches will be described along with available software.


Track 3: Multiple time scales, APC models and sampling from cohort studies (Mark Rutherford, Anna Johansson)

Description: This track will extend different time aspects of cohort studies, such as time scales, measures and modelling of rates over time. Cohort data can often be described according to multiple time scales, e.g. age, calendar time and time-since-diagnosis, by which rates can be estimated and compared over different exposure levels. Age–period–cohort (APC) models provide a framework for modeling trends in incidence and mortality rates over time scales. We will concentrate on analyzing, interpreting and presenting time trends according to the effects of age, calendar period and birth cohort. Concepts like drift, identifiability and splines will be discussed. The emphasis will be on practical applications to real cancer incidence data, and the use of the apcfit package to fit age, period and cohort effects via restricted cubic spline models in Stata. We will also cover sampling from cohort studies, including the Nested Case Control design (with matching for time) and the Case-Cohort design (without matching for time). We will compare the sampling and analysis of the two study designs, and discuss in which situations each design in useful. Examples from applications in real studies will be presented and practical exercises will be provided.