1 / 23

Improved life tables: by geography, socio-economic status…

Improved life tables: by geography, socio-economic status…. Bernard Rachet and Michel Coleman. smoothed rates. Methods of smoothing life tables. Model life tables Brass (Ewbank) Kostaki Smoothing formulae / interpolation Elandt-Johnson Akima Flexible multivariable models Splines.

amalie
Download Presentation

Improved life tables: by geography, socio-economic status…

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Improved life tables: by geography, socio-economic status… Bernard Rachet and Michel Coleman Methods and applications for population-based survival 20-21 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. Baseline mortality function Non-proportional effects Effect of deprivation on the baseline mortality function Poisson regression Model effects of covariates on observed mortality rates (nmx obs)

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

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

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

  9. Results 2/4 • Less variability with the quality of data

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

  11. Results 4/4 • Better estimation of relative survival

  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. Background mortality by deprivationmales, England and Wales, 1990-92 Rate per 100,000 100,000 10,000 1,000 Most deprived 100 Least deprived 10 0 10 20 30 40 50 60 70 80 90 100 Age at death (years)

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

  15. Rectal cancer survival, men, England and Wales 60 55 50 Relative survival (%) 45 1996-99 40 1991-95 35 1986-90 30 2 3 4 Deprived Affluent Deprivation category

  16. 100 expected 90 80 Survival (%) relative 70 60 observed 50 Rich 2 3 4 Poor Socio-economic category

  17. Affluent group: lowbackground mortalityDeprivation 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 National life table Deprivation-specific life table Affluent Deprived

  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 ?

More Related