Cancer Survival: Principles, Methods And Applications
Cancer survival: principles, methods and applicationsIn collaboration with the International Agency for Research on Cancer (IARC)22 - 26 June ivityRegistration (tea and coffee will be available)Introduction and logisticsMichel ColemanSession 111:00-11:30Cancer survival research and cancer policy - 1Michel ColemanMorning Coffee11:30-12:15Session 2Population-based measures of cancer burdenMelanie Morris12:15-13:15Session 3Introduction to survival analysisBernard Rachet13:15-14:0014:00-15:30Welcome lunch in South CourtyardSession 415:30-16:00Population-based cancer survival: concepts andestimationMaja Pohar PermeAfternoon Tea16:00-17:00Session 5Practical 1: IntroductionStudent groups with facultyTUESDAY8:30-9:00SessionSession 6ActivityQuestions and Answers from Day 1All students and faculty9:00-10:30Session 7Population-based cancer survival: data quality andquality controlClaudia Allemani/Rhea Harewood/Helena Carreira10:30-11:0011:00-12:30Morning CoffeeSession 8Practical 2: Estimating cancer survivalStudent groups with faculty
12:30-13:30Lunch break13:30-14:15Session 9Age-standardisation of cancer survivalManuela Quaresma14:15-15:15Session 10Impact on cancer survival estimates of using differentlife tablesLaura Woods/Devon Spika15:15-15:4515:45-17:00Afternoon TeaSession 11WEDNESDAY Session08:30-9:00Session 129:00-10:00Session 1310:00-10:3010:30-12:00ActivityQuestions and Answers from Day 2All students and facultyPeriod analysis and “prediction” of survivalRuoran LiMorning CoffeeSession 1412:00-13:0013:00-14:30Practical 3: Impact on cancer survival estimates ofusing different life tablesStudent groups with facultyPractical 4: Period analysis and “prediction” of survivalStudent groups with facultyLunch breakSession 1514:30-15:15Modelling net survivalPaul DickmanCourse photograph followed by tea break15:15-17:00Session 16Practical 5: Modelling net survivalStudent groups with facultyTHURSDAY8:30-9:00SessionSession 17ActivityQuestions and Answers from Day 3All students and faculty9:00-10:00Session 18Estimating net survival – past and presentPaul Dickman10:00-10:30Morning Coffee
10:30-12:00Session 1912:00-13:0013:00-14:30Crude probability of death: estimation and applicationsPaul DickmanLunch breakSession 2014:30-15:00Missing data and the estimation of cancer survivalBernard RachetAfternoon Tea15:00-16:30Session 21Practical 6: Handling missing data in survival analysisStudent groups and faculty16:30-17:30Session 22Cancer survival - participants’ case studiesAll students and faculty; facilitated by AudreyBonaventureFRIDAY8:30-9:00SessionSession 23ActivityQuestions and Answers from Day 4All students and faculty9:00-10:00Session 24Data visualisation: funnel plots and mapping for cancersurvivalManuela Quaresma10:00-11:00Session 25Excess hazard regression models: general principlesand practical adviceAurelien Belot/Camille Maringe11:00-11:3011:30-12:30Morning CoffeeSession 2612:30-13:30International comparisons of cancer survivalClaudia AllemaniLunch in South Courtyard13:30-14:30Session 27Secondary measures of cancer survivalSarah Walters14:30-15:30Session 28Cancer survival research and cancer policy - 2Michel Coleman15:30-16:00Session 29Tools for survival analysesAll students and faculty
Outline of contents of each sessionIntroduction and logistics Objectives of courseIntroduction of faculty membersIntroduction of course participantsOutline of course structurePresentation of course materialsAnnouncementsBackSession 1Cancer survival research and cancer policy – 1 Why do we study cancer survival? An introduction to the wider public, public health andhealth policy applicationsThe public interest and communication with the publicEvaluation of cancer treatment and cancer survival in the population settingEvaluation of cancer control policyBackSession 2Population-based measures of cancer burden Introduction to concept of cancer burdenThe need for population-based cancer registry data for incidence and survivalPrinciples of population-based measures of cancer burdenReview of incidence, prevalence, mortalityIntroduction to survivalRelationship between measures of cancer burdenBackSession 3Introduction to survival analysis Introduction to time-to-event dataDefinition of the survival and hazard functionsActuarial and Kaplan-Meier methods for estimation of the survival functionPoisson regressionCox proportional hazard modelBack
Session 4Population-based cancer survival: concepts and estimation Cause of death information and population mortality tablesObserved survivalCrude and net survivalRelative survival ratioMethods of estimationSpecific aspects in net survival estimationBackSession 5Practical 1 – Introduction This session will include an introduction to Stata and setting up for practicals 2-6The session will be led by one of the course faculty and tutors will be available to provideassistanceBackSession 6Questions and Answers from Day 1 An informal question-and-answer session on any topic covered on the first day. Allstudents and faculty will be invited to participateBackSession 7Population-based cancer survival: data quality and quality control Background to population-based cancer registration: regional and national registries,general and specialised registries Registration systems: sources of data, active and passive follow-up Data quality indicators for survival Purpose of quality controls:o to ensure robust comparisons of survivalo to document data quality for external review Types of quality controls:o on variables (compliance with a study protocol)o on records (logical coherence)o on data sets (frequency distributions, summary measures,.) Improving comparability through standard coding approaches to topography, morphologyand stageBack
Session 8Practical 2 – Estimating cancer survival This practical session will take place in a computer room, where participants will have theopportunity to do practical exercises around the themes discussed in the accompanyinglecture. A practical lead will facilitate the session and tutors will be on hand to provideassistance. Exercise solutions will be provided during the session.BackSession 9Age Standardisation of cancer survival Importance of age standardisationAge-standardisation methodExample of application and interpretationChoice of standard cancer populationExtension to multi-factor standardisationBackSession 10Impact on cancer survival estimates of using different life tables Life tables as a cross-sectional summary of recent mortalityRole of life tables in net survival estimationUtility of life tables for population sub-groups in net survival estimationAppropriate selection of life tables in net survival estimationBackSession 11Practical 3 - Impact on cancer survival estimates of using different life tables This practical session will take place in a computer room, where participants will have theopportunity to do practical exercises around the themes discussed in the accompanyinglecture. A practical lead will facilitate the session and tutors will be on hand to provideassistance. Exercise solutions will be provided during the session.BackSession 12Questions and Answers from Day 2 An informal question-and-answer session on any topic covered on the second day. Allstudents and faculty will be invited to participateBack
Session 13Period analysis and “prediction” of survival Cohort, complete and period approaches to cancer survival analysisPrinciples and theoretical basis of period analysis: analogy with expectation of lifeApplication and interpretation of period survival estimatesDevelopments in period analysis, including hybrid analysisBackSession 14Practical 4 – Period analysis and “prediction” of survival This practical session will take place in a computer room, where participants will have theopportunity to do practical exercises around the themes discussed in the accompanyinglecture. A practical lead will facilitate the session and tutors will be on hand to provideassistance. Exercise solutions will be provided during the session.BackSession 15Modelling net survival Outcome in survival analysis can be expressed as either a survival proportion or mortalityrate (hazard)Net mortality can be estimated and modelled in a cause-specific or relative survivalframework. We model on the hazard scale; cause-specific or excess.Three modelling approaches will be presented and their close similarities highlighted; Coxregression, Poisson regression, and flexible parametric models. The latter two can be usedto model both cause-specific and excess mortality whereas Cox regression cannot.The three approaches are conceptually very similarThe proportional hazards assumptionBackSession 16Practical 5 – Modelling net survival This practical session will take place in a computer room, where participants will have theopportunity to do practical exercises around the themes discussed in the accompanyinglecture. A practical lead will facilitate the session and tutors will be on hand to provideassistance. Exercise solutions will be provided during the session.BackSession 17Questions and Answers from Day 3 An informal question-and-answer session on any topic covered on the third day. Allstudents and faculty will be invited to participateBack
Session 18Estimating net survival – past and present Recap from sessions 16 and 17 on estimators of net survival.Philosophy behind the estimators. Why they were developed and how recommendationshave changed over time.Which estimator should one use in practice?BackSession 19Crude probability of death: estimation and applications Introduction to the concept of competing risks.Net survival, and net probabilities of death, are estimated for a hypothetical world whereone cannot die of causes other than the cancer of interest.One can also estimate so-called crude probabilities, which represent the probability ofdying of a specific cancer in the real world (where it’s possible to die of other causes).Estimating crude probabilities in a life table framework (implemented in -strs-).Estimating crude & net probabilities of death in a model-based framework.In which world should we work (real or hypothetical)?BackSession 20Missing data and the estimation of cancer survival Missing data, a recurrent problem: not to be ignored!Missing data mechanismsMethods for handling missing dataMultiple imputationModelling of excess hazard and net survival in the presence of incomplete dataBackSession 21Practical 6 – Handling missing data in survival analysis This practical session will take place in a computer room, where participants will have theopportunity to do practical exercises around the themes discussed in the accompanyinglecture. A practical lead will facilitate the session and tutors will be on hand to provideassistance. Exercise solutions will be provided during the session.Back
Session 22Cancer survival - participants' case studiesThis session offers course participants the opportunity to raise unresolved questions orpractical problems in cancer survival research that they may have encountered, for discussionwith faculty and other participants.You are invited to offer a short presentation. The presentation may be based on analysis ofyour own data, but you may also want to raise a theoretical or applied question about cancersurvival – this may involve theory, statistics, computing, data quality, public health or healthpolicy. If many presentations are offered, faculty members will make a selection. Three slides(maximum!) and five minutes to make your point, with 5-10 minutes’ wider discussion,depending on the number of proposed presentations.BackSession 23Questions and Answers from Day 4 An informal question-and-answer session on any topic covered on the fourth day. Allstudents and faculty will be invited to participateBackSession 24Data visualisation: funnel plots and mapping for cancer survival User needs and demands for dataOutcome indicators and interpretation of ranked resultsPrinciples of mapping cancer survivalMapping temporal change and the impact of policy changes on survivalPrinciples of funnel plots for institutional comparisonApplication of funnel plots to explore regional and temporal variations in cancer survivaland related measuresBackSession 25Excess hazard regression models: general principles and practical advice General principles of regression modelsAdvantages and drawbacks of regression modelsPractical advice on model building strategySelection and presentation of meaningful results from regression modelsBack
Session 26International comparisons of cancer survival EUROCARE, CONCORD and other international collaborative studiesImportance of age standardisation"Low-resolution", "high-resolution" and "patterns of care" studiesImpact of data quality and bias on the interpretation of international differences in survivalThe issue of national representativenessBackSession 27Secondary measures of cancer survival Communicating concepts and findings to patients, the public and policy makers‘Avoidable deaths’ and population ‘cure’ as ‘secondary measures’ of cancer survival,useful for communicationEstimation and interpretationExamples of useBackSession 28Cancer survival research and cancer policy – 2 Are cancer survival statistics of any use for public health and health policy?Confidentiality and consent in cancer registrationPublic health and policy impact of ethnic, socio-economic and international comparisonsof cancer survivalWorld Cancer Declaration 2013 and WHO policy on non-communicable diseasesBackSession 29Tools for survival analyses Availability and compatibility of software packages for the estimation of cancer survivalSTNS, SURV3, RELSURV, STREL, SEER*Stat, STRS, . in Stata, SAS or RImplementation of survival analysis packages in public-use databases such as SEER*Stat(USA) and the Cancer Information System (UK)Availability of life tables and other tools for survival analysisResidual questions about theoretical issues covered during the courseBack to top of programme
Practical 2 – Estimating cancer survival This practical session will take place in a computer room, where participants will have the opportunity to do practical exercises around the themes discussed in the accompanying lecture. A practical lead wi
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