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Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes


Autoři: Shih-Yung Su aff001;  Wen-Chung Lee aff001
Působiště autorů: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan aff001;  Innovation and Policy Center for Population Health and Sustainable Environment, College of Public Health, National Taiwan University, Taipei, Taiwan aff002;  Taiwan Cancer Registry, Taipei, Taiwan aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226678

Souhrn

Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973–2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.

Klíčová slova:

Age groups – Antigen-presenting cells – Cohort studies – Death rates – Prostate cancer – Simulation and modeling – Statistical models


Zdroje

1. Su SY, Huang JY, Ho CC, Liaw YP. Evidence for cervical cancer mortality with screening program in Taiwan, 1981–2010: age-period-cohort model. BMC Public Health. 2013;13:13. doi: 10.1186/1471-2458-13-13 23297757

2. Lee LT, Huang HY, Huang KC, Chen CY, Lee WC. Age-period-cohort analysis of hepatocellular carcinoma mortality in Taiwan, 1976–2005. Ann Epidemiol. 2009;19(5):323–8. doi: 10.1016/j.annepidem.2008.12.013 19362276.

3. Mdzinarishvili T, Gleason MX, Sherman S. Estimation of hazard functions in the log-linear age-period-cohort model: application to lung cancer risk associated with geographical area. Cancer Inform. 2010;9:67–78. doi: 10.4137/cin.s4522 20467481

4. Gangnon RE, Sprague BL, Stout NK, Alagoz O, Weedon-Fekjaer H, Holford TR, et al. The contribution of mammography screening to breast cancer incidence trends in the United States: an updated age-period-cohort model. Cancer Epidemiol Biomarkers Prev. 2015;24(6):905–12. doi: 10.1158/1055-9965.EPI-14-1286 25787716

5. Weedon-Fekjaer H, Bakken K, Vatten LJ, Tretli S. Understanding recent trends in incidence of invasive breast cancer in Norway: age-period-cohort analysis based on registry data on mammography screening and hormone treatment use. BMJ. 2012;344:e299. doi: 10.1136/bmj.e299 22290099

6. Houweling H, Wiessing LG, Hamers FF, Termorshuizen F, Gill ON, Sprenger MJ. An age-period-cohort analysis of 50,875 AIDS cases among injecting drug users in Europe. Int J Epidemiol. 1999;28(6):1141–8. doi: 10.1093/ije/28.6.1141 10661660.

7. Bell A, Jones K. The impossibility of separating age, period and cohort effects. Soc Sci Med. 2013;93:163–5. doi: 10.1016/j.socscimed.2013.04.029 23701919

8. Fienberg SE. Cohort analysis’ unholy quest: a discussion. Demography. 2013;50(6):1981–4; discussion 5–8. doi: 10.1007/s13524-013-0251-z 24132742.

9. Te Grotenhuis M, Pelzer B, Luo L, Schmidt-Catran AW. The intrinsic estimator, alternative estimates, and predictions of mortality trends: a comment on masters, hummer, powers, beck, lin, and finch. Demography. 2016;53(4):1245–52. doi: 10.1007/s13524-016-0476-8 27173796

10. Luo L. Assessing validity and application scope of the intrinsic estimator approach to the age-period-cohort problem. Demography. 2013;50(6):1945–67. doi: 10.1007/s13524-013-0243-z 24072610

11. Glenn ND. Cohort analysis. 2nd ed. London: SAGE Publications; 2005.

12. Luo LY. Paradigm shift in age-period-cohort analysis: a response to Yang and Land, O’Brien, Held and Riebler, and Fienberg. Demography. 2013;50(6):1985–8. doi: 10.1007/s13524-013-0263-8

13. Bell A, Jones K. Another’futile quest’? A simulation study of Yang and Land’s Hierarchical Age-Period-Cohort model. Demogr Res. 2014;30:333–60.

14. Bell A, Jones K. The hierarchical age–period–cohort model: Why does it find the results that it finds? Quality & Quantity. 2017. doi: 10.1007/s11135-017-0488-5 29568132

15. Viel JF, Rymzhanova R, Fournier E, Danzon A. Trends in invasive breast cancer incidence among French women not exposed to organized mammography screening: an age-period-cohort analysis. Cancer Epidemiol. 2011;35(6):521–5. doi: 10.1016/j.canep.2011.04.002 21621498.

16. Hsiao CC, Chuang JH, Tiao MM, Sheen JM, Shieh CS. Patterns of hepatoblastoma and hepatocellular carcinoma in children after universal hepatitis B vaccination in taiwan: a report from a single institution in southern Taiwan. J Pediatr Hematol Oncol. 2009;31(2):91–6. doi: 10.1097/MPH.0b013e31818b3784 19194190.

17. Lee CL, Ko YC. Hepatitis B vaccination and hepatocellular carcinoma in Taiwan. Pediatrics. 1997;99(3):351–3. doi: 10.1542/peds.99.3.351 9041286.

18. Rubin MM. Antenatal exposure to DES: lessons learned …future concerns. Obstet Gynecol Surv. 2007;62(8):548–55. doi: 10.1097/01.ogx.0000271138.31234.d7 17634156.

19. Hoover RN, Hyer M, Pfeiffer RM, Adam E, Bond B, Cheville AL, et al. Adverse health outcomes in women exposed in utero to diethylstilbestrol. N Engl J Med. 2011;365(14):1304–14. doi: 10.1056/NEJMoa1013961 21991952.

20. Holford TR. The estimation of age, period and cohort effects for vital rates. Biometrics. 1983;39(2):311–24. 6626659.

21. Fu WJJ. Ridge estimator in singular design with application to age-period-cohort analysis of disease rates. Commun Stat-Theor M. 2000;29(2):263–78.

22. Knight K, Fu WJ. Asymptotics for Lasso-type estimators. Ann Stat. 2000;28(5):1356–78.

23. Lee WC, Lin RS. Modelling the age-period-cohort trend surface. Biometrical J. 1996;38(1):97–106.

24. Tu YK, Davey Smith G, Gilthorpe MS. A new approach to age-period-cohort analysis using partial least squares regression: the trend in blood pressure in the Glasgow Alumni cohort. PLoS One. 2011;6(4):e19401. doi: 10.1371/journal.pone.0019401 21556329

25. Tu YK, Kramer N, Lee WC. Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis. Epidemiology. 2012;23(4):583–93. doi: 10.1097/EDE.0b013e31824d57a9 22407139.

26. Pelzer B, te Grotenhuis M, Eisinga R, Schmidt-Catran AW. The non-uniqueness property of the intrinsic estimator in APC models. Demography. 2015;52(1):315–27. doi: 10.1007/s13524-014-0360-3 25550143.

27. Osmond C, Gardner MJ. Age, period and cohort models applied to cancer mortality rates. Stat Med. 1982;1(3):245–59. doi: 10.1002/sim.4780010306 7187097.

28. Lee WC, Lin RS. Autoregressive age-period-cohort models. Stat Med. 1996;15(3):273–81. doi: 10.1002/(SICI)1097-0258(19960215)15:3<273::AID-SIM172>3.0.CO;2-R 8643885.

29. Clayton D, Schifflers E. Models for temporal variation in cancer rates. I: Age-period and age-cohort models. Stat Med. 1987;6(4):449–67. doi: 10.1002/sim.4780060405 3629047.

30. Clayton D, Schifflers E. Models for temporal variation in cancer rates. II: Age-period-cohort models. Stat Med. 1987;6(4):469–81. doi: 10.1002/sim.4780060406 3629048.

31. Chauvel L, Leist AK, Ponomarenko V. Testing persistence of cohort effects in the epidemiology of suicide: an age-period-cohort hysteresis model. Plos One. 2016;11(7). doi: 10.1371/journal.pone.0158538 27442027

32. Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973–2013), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2016, based on the November 2015 submission.

33. Cook PJ, Doll R, Fellingham SA. A mathematical model for the age distribution of cancer in man. Int J Cancer. 1969;4(1):93–112. doi: 10.1002/ijc.2910040113 5346480.

34. Gronberg H. Prostate cancer epidemiology. Lancet. 2003;361(9360):859–64. doi: 10.1016/S0140-6736(03)12713-4 12642065.

35. Crawford ED. Epidemiology of prostate cancer. Urology. 2003;62(6 Suppl 1):3–12. doi: 10.1016/j.urology.2003.10.013 14706503.

36. Welch HG, Albertsen PC. Prostate cancer diagnosis and treatment after the introduction of prostate-specific antigen screening: 1986–2005. J Natl Cancer Inst. 2009;101(19):1325–9. doi: 10.1093/jnci/djp278 19720969

37. Hsing AW, Tsao L, Devesa SS. International trends and patterns of prostate cancer incidence and mortality. Int J Cancer. 2000;85(1):60–7. doi: 10.1002/(sici)1097-0215(20000101)85:1<60::aid-ijc11>3.0.co;2-b 10585584.

38. Etzioni R, Penson DF, Legler JM, di Tommaso D, Boer R, Gann PH, et al. Overdiagnosis due to prostate-specific antigen screening: Lessons from US prostate cancer incidence trends. J Natl Cancer I. 2002;94(13):981–90.

39. Stanford JL, Stephenson RA, Coyle LM, Cerhan J, Correa R, Eley JW, et al. Prostate cancer trends 1973–1995, SEER Program, National Cancer Institute. NIH Pub. 1999;No. 99–4543. Bethesda, MD.

40. Potosky AL, Miller BA, Albertsen PC, Kramer BS. The role of increasing detection in the rising incidence of prostate cancer. JAMA. 1995;273(7):548–52. 7530782.

41. Legler JM, Feuer EJ, Potosky AL, Merrill RM, Kramer BS. The role of prostate-specific antigen (PSA) testing patterns in the recent prostate cancer incidence decline in the United States. Cancer Causes Control. 1998;9(5):519–27. doi: 10.1023/a:1008805718310 9934717.

42. Baade PD, Yu XQ, Smith DP, Dunn J, Chambers SK. Geographic disparities in prostate cancer outcomes—review of international patterns. Asian Pac J Cancer Prev. 2015;16(3):1259–75. doi: 10.7314/apjcp.2015.16.3.1259 25735366.

43. Gann PH. Risk factors for prostate cancer. Rev Urol. 2002;4 Suppl 5:S3–S10. 16986064

44. O’Brien RM. Age–period–cohort models and the perpendicular solution. Epidemiologic Methods. 2015;4(1):87–99.

45. Fu WJ. Constrained estimators and consistency of a regression model on a Lexis diagram. J Am Stat Assoc. 2016;111(513):180–99. doi: 10.1080/01621459.2014.998761

46. Carstensen B. Age-period-cohort models for the Lexis diagram. Statistics in Medicine. 2007;26(15):3018–45. doi: 10.1002/sim.2764 17177166

47. Gulevich RG, Shikhevich SG, Konoshenko MY, Kozhemyakina RV, Herbeck YE, Prasolova LA, et al. The influence of social environment in early life on the behavior, stress response, and reproductive system of adult male Norway rats selected for different attitudes to humans. Physiol Behav. 2015;144:116–23. doi: 10.1016/j.physbeh.2015.03.018 25784612

48. Suglia SF, Ryan L, Bellinger D, Wright R. The influence of the social and physical environment on child behavior. Epidemiology. 2006;17(6):S387–S. doi: 10.1097/00001648-200611001-01031

49. Johnson RA, Gerstein DR. Age, period, and cohort effects in marijuana and alcohol incidence: United States females and males, 1961–1990. Subst Use Misuse. 2000;35(6–8):925–48. doi: 10.3109/10826080009148427 10847217

50. Schwadel P. Age, period, and cohort effects on religious activities and beliefs. Soc Sci Res. 2011;40(1):181–92.

51. Yang Y. Social inequalities in happiness in the United States, 1972 to 2004: an age-period-cohort analysis. American Sociological Review. 2008;73(2):204–26. doi: 10.1177/000312240807300202

52. Kerr WC, Greenfield TK, Bond J, Ye Y, Rehm J. Age-period-cohort modelling of alcohol volume and heavy drinking days in the US National Alcohol Surveys: divergence in younger and older adult trends. Addiction. 2009;104(1):27–37. doi: 10.1111/j.1360-0443.2008.02391.x 19133886

53. Clark AK, Eisenstein MA. Interpersonal trust: An age-period-cohort analysis revisited. Soc Sci Res. 2013;42(2):361–75. doi: 10.1016/j.ssresearch.2012.09.006 23347482

54. Robinson RV, Jackson EF. Is trust in others declining in America? An age-period-cohort analysis. Soc Sci Res. 2001;30(1):117–45.


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