European Journal of Epidemiology

, Volume 31, Issue 11, pp 1091–1099 | Cite as

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer

  • Igor Karp
  • Marie-Pierre Sylvestre
  • Michal Abrahamowicz
  • Karen Leffondré
  • Jack Siemiatycki
CLINICAL EPIDEMIOLOGY

Abstract

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case–control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a ‘non-nested’ case–control study. We used data from a case–control study conducted in Montreal, Canada in 1996–2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and ‘shrinkage.’ The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting (‘shrinkage’ factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method’s risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Keywords

Case–control study Etiologic research Prognostic research Prognostication Risk assessment Absolute risk Lung cancer Logistic regression 

References

  1. 1.
    Kannel W, Wolf P, Garrison R. The Framingham study: an epidemiological investigation of cardiovascular disease. Washington, DC: US Department of Commerce; 1988.Google Scholar
  2. 2.
    Miettinen OS, Karp I. Epidemiological research: an introduction. Dordrecht: Springer; 2012.CrossRefGoogle Scholar
  3. 3.
    Karp I, Miettinen OS. The essentials of etiological research for preventive medicine. Eur J Epidemiol. 2014;29:455–7.CrossRefPubMedGoogle Scholar
  4. 4.
    Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009.CrossRefGoogle Scholar
  5. 5.
    Machin D, Campbell MJ. The design of studies for medical research. New York: Wiley; 2005.CrossRefGoogle Scholar
  6. 6.
    Fletcher RH, Fletcher SW, Fletcher GS. Clinical epidemiology: the essentials. 5th ed. Philadelphia: Lippincott Williams & Wilkins; 2014.Google Scholar
  7. 7.
    Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879–86.CrossRefPubMedGoogle Scholar
  8. 8.
    Langholz B, Borgan O. Estimation of absolute risk from nested case–control data. Biometrics. 1997;53:767–74.CrossRefPubMedGoogle Scholar
  9. 9.
    Benichou J, Gail M. Methods of inference for estimates of absolute risk derived from population-based case–control studies. Biometrika. 1995;51:182–94.CrossRefGoogle Scholar
  10. 10.
    Pintos J, Parent ME, Richardson L, Siemiatycki J. Occupational exposure to diesel engine emissions and risk of lung cancer: evidence from two case–control studies in Montreal, Canada. Occup Environ Med. 2012;69:787–92.CrossRefPubMedGoogle Scholar
  11. 11.
    Siemiatycki J, Day NE, Fabry J, Cooper JA. Discovering carcinogens in the occupational environment: a novel epidemiologic approach. J Natl Cancer Inst. 1981;66:217–25.PubMedGoogle Scholar
  12. 12.
    Siemiatycki J, Wacholder S, Richardson L. Discovering carcinogens in the occupational environment: methods of data collection and analysis of a large case–referent monitoring system. Scand J Work Environ Health. 1987;13:486–92.CrossRefPubMedGoogle Scholar
  13. 13.
    Siemiatycki J, Fritschi L, Nadon L, Gerin M. Reliability of an expert rating procedure for retrospective assessment of occupational exposures in community-based case–control studies. Am J Ind Med. 1997;31:280–6.CrossRefPubMedGoogle Scholar
  14. 14.
  15. 15.
    Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. New York: Lippincott-Raven Publishers; 2008.Google Scholar
  16. 16.
    Leffondré K, Abrahamowicz M, Xiao Y, Siemiatycki J. Modelling smoking history using a comprehensive smoking index: application to lung cancer. Stat Med. 2006;25:4132–46.CrossRefPubMedGoogle Scholar
  17. 17.
    Steyerberg EW, Eijkemans MJC, Habbema JDF. Application of shrinkage techniques in logistic regression analysis: a case study. Stat Neerl. 2001;55:76–88.CrossRefGoogle Scholar
  18. 18.
    Harrell FE. Regression modeling strategies: with application to linear models, logistic regression, and survival analysis. New York: Springer; 2001.CrossRefGoogle Scholar
  19. 19.
    Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst. 2005;97:715–23.CrossRefPubMedGoogle Scholar
  20. 20.
    Tammemagi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368:728–36.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med. 2013;369:245–54.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Cassidy A, Myles JP, van Tongeren M, et al. The LLP risk model: an individual risk prediction model for lung cancer. Br J Cancer. 2008;98:270–6.CrossRefPubMedGoogle Scholar
  23. 23.
    Raji OY, Duffy SW, Arbaje OF, et al. Predictive accuracy of the Liverpool Lung Project model for stratifying patients for computed tomography screening for lung cancer: a case–control and cohort validation study. Ann Intern Med. 2012;157:242–50.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Tammemagi CM, Pinsky PF, Caporaso NE, et al. Lung cancer risk prediction: prostate, lung, colorectal and ovarian cancer screening trial models and validation. J Natl Cancer Inst 2011;103:1058–68.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Cassidy A, Duffy SW, Myles JP, et al. Lung cancer risk prediction: a tool for early detection. Int J Cancer. 2007;120:1–6.CrossRefPubMedGoogle Scholar
  26. 26.
    Park S, Nam BH, Yang HR, et al. Individualized risk prediction model for lung cancer in Korean men. PLoS ONE. 2013;8:e54823.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Spitz MR, Hong WK, Amos CI, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst. 2007;99:715–26.CrossRefPubMedGoogle Scholar
  28. 28.
    Spitz MR, Etzel CJ, Dong Q, et al. An expanded risk prediction model for lung cancer. Cancer Prev Res (Phila Pa). 2008;1:250–4.CrossRefGoogle Scholar
  29. 29.
    Bach PB, Kattan MW, Thornquist MD. Variations in lung cancer risk among smokers. J Natl Cancer Inst. 2003;95:470–8.CrossRefPubMedGoogle Scholar
  30. 30.
    Bach PB, Elkin EB, Pastorino U. Benchmarking lung cancer mortality rates in current and former smokers. Chest. 2004;126:1742–9.CrossRefPubMedGoogle Scholar
  31. 31.
    Siemiatycki J. Synthesizing the lifetime history of smoking. Cancer Epidemiol Biomarkers Prev. 2005;14:2294–5.CrossRefPubMedGoogle Scholar
  32. 32.
    Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:b605.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Igor Karp
    • 1
    • 2
  • Marie-Pierre Sylvestre
    • 2
    • 3
  • Michal Abrahamowicz
    • 4
    • 5
  • Karen Leffondré
    • 6
  • Jack Siemiatycki
    • 2
    • 3
  1. 1.Department of Epidemiology and Biostatistics, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonCanada
  2. 2.Department of Social and Preventive Medicine, School of Public HealthUniversity of MontrealMontrealCanada
  3. 3.Health Risks DivisionCentre de recherche du CHUMMontrealCanada
  4. 4.Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealCanada
  5. 5.Division of Clinical EpidemiologyResearch Institute of the McGill University Health CentreMontrealCanada
  6. 6.ISPED, Centre INSERM U897-Epidemiology-BiostatisticsUniversity of BordeauxBordeauxFrance

Personalised recommendations