1. Improved life tables: by geography, socio-economic status… Bernard Rachet and Michel Coleman Methods and applications for population-based survival20-21 September 2010

2. smoothed rates

3. Methods of smoothing life tables Model life tables –Brass (Ewbank) –Kostaki Smoothing formulae / interpolation –Elandt-Johnson –Akima Flexible multivariable models –Splines

4. Poisson regression Baseline mortality function Effect of deprivation on the baseline mortality function Model effects of covariates on observed mortality rates ( n m x obs ) Non-proportional effects

5. Objective and methods Goal: generating complete, smoothed, variable-specific and national life tables from sparse data Method:  Start from a “true” complete life table (England & Wales)  Draw 100 samples (20%, 10%, 1%)  Generate different datasets complete or abridged up to 80 or 100 years of age  Estimate complete smoothed life tables using three methods

6. Univariable Elandt-Johnson Multivariable Flexible regression of the logit of lx on a standard life table Flexible Poisson Model  Both using spline functions Models

7. Results 1/4 “Truth” Flexible Poisson Regression Elandt-Johnson From observed abridged up to 80 years, group 5, men, 1% sample Using the flexible Poisson model we observe  Less variability in the results

8. From observed abridged up to 80 years, national, men, 1% sample “Truth” Flexible Poisson Regression Elandt-Johnson

9. Results 2/4  Less variability with the quality of data From a 20% sample National Life Tables Best available data (C100 or AB95) Least Sum of Squares Flexible PoissonElandt-JohnsonRegression All age LSS min000 mean0.00007690.00367920.0017946 max0.00098480.06967480.0129517 From a 1% sample National Life Tables Worst available data (AB80) Least Sum of Squares Flexible PoissonElandt-JohnsonRegression All age LSS min000 mean0.00180730.01764380.1302963 max0.04785593.2746332.206511

10. Results 3/4  Better estimation of life expectancy From abridged up to 80 years, group 3, men, 1% sample Regression Flexible Poisson Regression Elandt-Johnson Poisson Elandt-Johnson

11. Results 4/4  Better estimation of relative survival From a 1% sample National Life Tables Best available data (C100 or AB95) Difference in Relative SurvivalFlexible PoissonElandt-JohnsonRegression BREAST 10 year relative survival min0.0020490.00739670.012661 mean0.18810.94042360.287412 max0.7272913.498680.735535

12. Life tables and cancer survival  Background mortality hazard (age, sex)  Reduce bias in survival comparisons  How finely to specify life tables by covariables:  Period or year of death  Country or region  Socio-economic status  Race and/or ethnicity  May require large number of life tables

13. 10 100 1,000 10,000 100,000 0102030405060708090100 Age at death (years) Rate per 100,000 Most deprived Least deprived Background mortality by deprivation males, England and Wales, 1990-92

14. Woods LM et al., J Epidemiol Comm Hlth 2005; 59: 115-20 Life expectancy: deprivation, sex, region

15. 1996-99 1991-95 1986-90 30 35 40 45 50 55 60 Relative survival (%) Affluent 234Deprived Deprivation category Rectal cancer survival, men, England and Wales

16. 50 60 70 80 90 100 Rich234Poor Socio-economic category Survival (%) expected relative observed

17. Affluent group: low background mortality Deprivation life table, lower survival estimate National life table Deprivation life table

18. Deprived group: high background mortality Deprivation life table, higher survival estimate Deprivation life table National life table

19. ‘Deprivation gap’ in relative survival: smaller with deprivation life tables Affluent Deprived National life table Deprivation-specific life table

20. National life table Region- and deprivation- specific life table

21. Life tables – “adjust” for exposure?  Underlies cancer and competing hazard of death  Carcinogenic exposure  High population attributable risk fraction  Tobacco, alcohol  Substantial hazard of non-cancer death  May complicate treatment and thus survival  Co-morbidity

22. Life tables – how to “adjust”?  Information on exposure at death certification  Available, complete, accurately recorded ?  Reliability of data from proxy of deceased ?  Crudity of exposure variable (binary) ?  Time-lag between exposure and death (relevance)?  Length of mortality data time series ?  Equivalent information on all cancer patients?  If not, assume that all patients were exposed ?  What threshold of hazard to decide when to adjust ?

23. Implications for principle of relative survival?  Co-morbidity affects non-cancer hazard  Standardised approach to life table adjustment ?  Relative survival adjusted for risk factors:  Interpretable ?  Comparable between cancers ?  Comparable between populations ?  Comparable over time ?  Intelligible ?