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

Volumn 6, Issue 4, 2011, Pages

  Tu, Yu Kang ^a   Smith, George Davey ^b   Gilthorpe, Mark S ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; ALUMNUS; ARTICLE; COHORT ANALYSIS; CONTROLLED STUDY; DIASTOLIC BLOOD PRESSURE; FEMALE; HUMAN; MALE; PARTIAL LEAST SQUARES REGRESSION; SEX DIFFERENCE; SYSTOLIC BLOOD PRESSURE; TREND STUDY; UNIVERSITY STUDENT; BLOOD PRESSURE; REGRESSION ANALYSIS; UNITED KINGDOM;

BLOOD PRESSURE; COHORT STUDIES; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; MALE; SCOTLAND;

EID: 79955686236     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0019401     Document Type: Article

References (49)

1. Glenn ND, (1976) Cohort analysts' futile quest: statistical attempts to separate age, period, and cohort effects. American Sociological Review 41: 900-905.
2. Glenn ND, (2003) Distinguishing age, period, and cohort effects. New York Kluwer Academic In Handbook of the Life Couse, edited by Mortimer JT, Shanahan MJ.
3. Glenn ND, (2005) Cohort analysis, 2nd ed. Thousand Oaks, CA Sage.
4. Holford TR, (1991) Understanding the effects of age, period, and cohort on incidence and mortality rates. Annual Reviews in Public Health 12: 425-457.
5. Yang Y, Fu WJ, Land KC, (2004) A Methodological Comparison of Age-Period-Cohort Models: The Intrinsic Estimator and Conventional Generalized Linear Models. Sociological Methodology 34: 75-110.
6. Yang Y, Schulhofer-Wohl S, Fu WJ, Land KC, (2008) The intrinsic estimator for age-period-cohort analysis: what it is and how to use it? American Journal of Sociology 113: 1697-1736.
7. Robertson C, Gandini S, Boyle P, (1999) Age-Period-Cohort Models: A Comparative Study of Available Methodologies. Journal of Clinical Epidemiology 52: 569-83.
8. Keyes KM, Utz RL, Robinson W, Li G, (2010) What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006. Social Science & Medicine 70: 1100-1108.
9. Feltbower RG, McKinney PA, Parslow RC, Stephenson CR, J. Bodansky HJ H, (2003) Type 1 diabetes in Yorkshire, UK: time trends in 0-14 and 15-29-year-olds, age at onset and age-period-cohort modeling. Diabetes Medicine 20: 437-441.
10. Tu YK, Clerehugh V, Gilthorpe MS, (2004) Collinearity in linear regression is a serious problem in oral health research. Eur J Oral Sci 112: 389-397.
11. Fox J, (2008) Applied regression analysis and generalized linear models. Thousand Oaks Sage pp. 307-332.
12. Goldstein H, (1979) Age, period and cohort effects - A confounded confusion. Journal of Applied Statistics 6: 19-24.
13. Robertson C, Boyle P, (1998) Age-period-cohort analysis of chronic disease rates. I: Modelling approach. Statist Med 17: 1305-1323.
14. Lee WC, Lin RS, (1996) Autoregressive age-period-cohort models. Statist Med 15: 273-281.
15. Lee WC, Lin RS, (1996) Modelling the Age-Period-Cohort Trend Surface. Biom J 38: 97-106.
16. Osmond C, Gardner MJ, (1982) Age, period and cohort models applied to cancer mortality rates. Statist Med 1: 245-259.
17. Hadi AS, Ling RF, (1998) Some cautionary notes on the use of principle components regression. Am Stat 52: 15-19.
18. Wold S, Sjostrom M, Eriksson L, (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst 58-109 130.
19. Boulesteix AL, Strimmer K, (2007) Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform 8: 32-44.
20. de Jong S, (1993) SIMPLS: an alternative approach to partial least squares regression. Chemometr Intell Lab Syst 18: 251-263.
21. Phatak A, de Jong S, (1997) The geometry of partial least squares. J Chemometrics 11: 311-338.
22. Hastie T, Tibshirani R, Friedman J, (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd ed. New York Springer.
23. McCarron P, Osaka M, McEwen J, Davey Smith G, (2001) Changes in blood pressure among students attending Glasgow University between 1948 and 1968: analyses of cross sectional surveys. BMJ 322: 885-889.
24. McCarron P, Davey Smith G, Okasha M, McEwen J, (1999) Life course exposure and later disease: a follow-up study based on medical examinations carried out in Glasgow University (1948-68). Public Health 113: 265-271.
25. Carroll JD, Green PE, (1997) Mathematical tools for applied multivariate analysis, 2nd edition. San Diego Academic Press.
26. Basilevsky A, (2005) Applied matrix algebra in the statistical sciences. New York Dover.
27. Harville DA, (2008) Matrix algebra from a statistician's perspective. New York Springer.
28. Kaspar MH, Ray WH, (1993) Partial least squares modelling as successive singular value decompositions. Computers Chem Engng 17: 985-989.
29. Tu YK, Woolston A, Baxter PD, Gilthorpe MS, (2010) Assessing the impact of body size in childhood and adolescence: An application of partial least squares regression. Epidemiology 21: 440-448.
30. Pringle RM, Rayner AA, (1971) Generalized inverse matrices with applications to statistics. London: Griffin pp. 80-117.
31. Rencher AC, Schaalje GB, (2008) Linear models in statistics, 2nd edition. Hoboken, New Jersey Wiley pp. 295-335.
32. Khuri AI, (2010) Linear model methodology. Boca Raton Chapman & Hall pp. 179-224.
33. Mazumdar S, Li CC, Bryce GR, (1980) Correspondence between a linear restriction and a generalized inverse in linear model analysis. Am Stat 34: 103-105.
34. Searle SR, Restrictions and Generalized Inverses in Linear Models. Am Stat 38: 53-54.
35. Wakeling IN, Morris JJ, (1993) A test of significance for partial least squares regression. J Chemometrics 7: 291-304.
36. Abdi H, (2010) Partial least squares regression and projection on latent structure regression (PLS Regression). 2 Wiley Interdisciplinary Reviews: Computational Statistics: 97-106.
37. Rosipal R, Krämer N, (2006) Overview and recent advances in partial least squares. New York Springer-Verlag pp. 34-51 In Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop (SLSFS 2005) (eds C. Saunders, M. Grobelnik, J. Gunn, J. Shawe-Taylor),.
38. Sundberg R, (2006) Continuum regression. In: Balakrishnan N, Read C, Vidakovic B, editors. Encyclopedia of Statistical Sciences. 2nd edition Hoboken, New Jersey Wiley pp. 1342-1345.
39. Eriksson L, Antti H, Holmes E, Johansson E, Lundstedt T, Wold S, (2003) Partial Least Squares (PLS) in Cheminformatics. Weinheim Wiley-VCH pp. 1134-1166 In Handbook of Chemoinformatics (eds J. Gasteiger).
40. Harrell F, (2001) Regression modeling strategies. New York Springer pp. 20-26.
41. Gluckman PD, Hanson MA, Cooper C, Thornburg KL, (2008) Effect of in utero and early-life conditions on adult health and disease. N Engl J Med 359: 61-73.
42. Gluckman PD, Hanson MA, Bateson P, Beedle AS, Law CM, et al. (2009) Towards a new developmental synthesis: adaptive developmental plasticity and human disease. Lancet 373: 1654-1657.
43. Davey Smith G, Leary S, Ness A, (2006) Could dehydration in infancy lead to high blood pressure? J Epidemiol Community Health 60: 142-3.
44. Lawlor DA, Davey Smith G, Mitchell R, Ebrahim S, (2006) Adult blood pressure and climate conditions in infancy: a test of the hypothesis that dehydration in infancy is associated with higher adult blood pressure. Am J Epidemiol 163: 608-14.
45. Ebrahim S, Davey Smith G, (1998) Lowering blood pressure: a systematic review of sustained effects of non-pharmacological interventions. J Public Health Med 20: 441-448.
46. O'Brien RM, (2011) The age-period-cohort conundrum as two fundamental problems. Qual Quant DOI 10.1007/s11135-010-9397-6.
47. Geleijnse JM, Hofman A, Witteman JC, Hazebroek AA, Valkenburg HA, et al. (1997) Long-term effects of neonatal sodium restriction on blood pressure. Hypertension 29: 913-917.
48. Barker M, Rayens W, (2003) Partial least squares discrimination. J Chemometrics 17: 166-173.
49. Chevallier S, Bertrand D, Kohler A, Courcoux P, (2006) Application of PLS-DA in multivariate image analysis. J Chemometrics 20: 221-229.