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The Statistical Analysis of Event Histories in Longitudinal Studies of Aging

Published online by Cambridge University Press:  29 November 2010

John P. Hirdes  and
K. Stephen Brown
John P. Hirdes
Affiliation:
University of Waterloo and Freeport Hospital
K. Stephen Brown
Affiliation:
University of Waterloo
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Abstract

Data from longitudinal studies have a number of advantages for gerontological research, but the effects of attrition and the increased complexity of data structures with multiple observations may pose some problems for statistical analysis. Proportional hazards models allow for the examination of event histories using all observations of a dependent variable, and these models can incorporate time-dependent covariates to increase their explanatory power. The assumptions and applications of event history analysis using proportional hazards models are described, and the analysis of mortality data from the Ontario Longitudinal Study of Aging provides a relevant example. Extensions of proportional hazards models and commercially available software are also discussed.

Résumé

Les données tirées d'études longitudinales possèdent un certain nombre d'avantages pour les recherches en gérontologie; toutefois, les effets de l'attrition et la complexité accrue des structures de données avec de multiples observations peuvent poser certains problèmes pour les analyses statistiques. Les modèles de risques proportionnels permettent un examen de l'histoire d'un événement par l'utilisation de toutes les observations d'une variable dépendante. Ces modèles peuvent comprendre des covariables afin d'accroître leur capacité d'explication. Des hypothèses et des applications de l'analyse de l'histoire d'un événement utilisant des risques proportionnels sont décrits. L'analyse des données sur la mortalité tirées de l'étude longitudinale du vieillissement menée en Ontario en constitue un exemple pertinent. Les extensions du modèle de risques proportionnels et les logiciels disponibles sur le marché sont également traités.

Keywords

Event HistoryLongitudinalStatistical ModelsMortalityAnalyse des événementslongitudinalemodèles statistiquesmortalité
Type
Articles
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 13 , Issue 3 , Automne/Fall 1994 , pp. 332 - 352
Copyright
Copyright © Canadian Association on Gerontology 1994

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References

Allison, P.D. (1984). Event History Analysis: Regression for Longitudinal Event Data. Newbury Park: Sage Publications.CrossRefGoogle Scholar
Birren, J.E. (1968). Principles of Research on Aging. pp. 545–551 In Neugarten, B.L. (Ed.), Middle-age and Aging. Chicago: University of Chicago Press.Google Scholar
Campbell, R.T. (1988). Integrating conceptualization, design, and analysis in panel studies of the life course. pp. 171–209 In Schaie, K.W., Campbell, R.T., Meredith, W., & Rawlings, S.C. (Eds.), Methodological Issues in Aging Research. New York: Springer Publishing.Google Scholar
Cox, D.R. (1970). Analysis of Binary Data. London: Methuen.Google Scholar
Cox, D.R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, B, 34, 187–220.Google Scholar
Deeg, D.J.H. (1989). Experiences from Longitudinal Studies of Aging: Conceptualization, Organization and Output. Nijmegen: Nederlands Instituut voor Gerontologie.Google Scholar
Deeg, D.J.H., & Van der Zanden, G.H. (1991). Experiences from longitudinal studies of aging: An international perspective. Journal of Cross-Cultural Gerontology, 6, 7–22.CrossRefGoogle ScholarPubMed
Dixon, W.J. (1983). BMDP Statistical Software. Berkeley: University of California Press.Google Scholar
Forbes, W.F., McPherson, B.D., & Shadbolt-Forbes, M.A. (1989). Validation of longitudinal studies: The case of the Ontario Longitudinal Study of Aging. Canadian Journal on Aging, 8, 51–67.CrossRefGoogle Scholar
Fox, A., & Irelan, L.M. (1989). Managing a longitudinal study: Lessons from the Social Security Administrations Retirement History Study. pp. 249–261 In Lawton, M.P. & Hertzog, A.R. (Eds.), Special Research Methods for Gerontology. Amityville, NY: Baywood Publishing.Google Scholar
Goudy, W.J. (1976). Non-response effects of relationships between variables. Public Opinion Quarterly, 40, 360–369.CrossRefGoogle Scholar
Hirdes, J.P., Brown, K.S., Vigoda, D.S., Forbes, W.F., & Crawford, L. (1986). The association between self reported income and perceived health based on the Ontario Longitudinal Study of Aging. Canadian Journal of Aging, 5, 189–204.CrossRefGoogle Scholar
Hirdes, J.P., Brown, K.S., Vigoda, D.S., Forbes, W.F., & Crawford, L. (1987). Health effects of cigarette smoking: Data from the Ontario Longitudinal Study of Aging. Canadian Journal of Public Health, 78, 13–17.Google Scholar
Hirdes, J.P., & Forbes, W.F. (1989). Estimates of the relative risk of mortality based on the Ontario Longitudinal Study of Aging. Canadian Journal on Aging, 8, 222–237.CrossRefGoogle Scholar
Hirdes, J.P., & Forbes, W.F. (1992). The importance of social relationships, socioe-conomic status and health practices with respect to mortality among health Ontario males. Journal of Clinical Epidemiology, 45, 175–182.CrossRefGoogle Scholar
Kalbfleisch, J.D., & Lawless, J.F. (1988). Likelihood analysis of multi-state models for disease incidence and mortality. Statistics in Medicine, 7, 149–160.CrossRefGoogle ScholarPubMed
Kalbfleisch, J.D., & Prentice, R.L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.Google Scholar
Kelsey, J.L., Thompson, W.D., & Evans, A.S. (1986). Methods in Observational Epidemiology. New York: Oxford University Press.Google Scholar
Lawless, J.F. (1982). Statistical Models and Methods for Lifetime Data. New York: Wiley.Google Scholar
Matthews, D.E., & Farewell, V.T. (1988). Using and Understanding Medical Statistics 2nd ed.Basel: Karger.Google Scholar
Prentice, R.L. & Farewell, V.T. (1986). Relative risk in odds ratio regression. Annual Review of Public Health, 7, 35–38.CrossRefGoogle ScholarPubMed
SAS Institute. (1990). SAS/STAT Users Guide; Version 6 4th ed.Cary, NC: SAS Institute.Google Scholar
SAS Institute, (1991). SAS/STAT Software: The PHREG Procedure SAS Technical Report P–217. SAS/STAT Users Guide. Cary, NC: SAS Institute.Google Scholar
Sharma, S.K., Tobin, J.D., & Brant, L.J. (1989). Attrition in the Baltimore Longitudinal Study of Aging during the first twenty years. pp. 233–247 In Lawton, M.P. & Hertzog, A.R. (Eds.), Special Research Methods for Gerontology. Amityville, NY: Baywood Publishing.Google Scholar
Stram, D.O., Wei, L.J., & Ware, J.H. (1988). Analysis of repeated ordered categorical outcomes with possibly missing observations and time-dependent covariates. Journal of the American Statistical Association, 83, 631–637.CrossRefGoogle Scholar
Sussman, M.B. (1964). Use of the longitudinal design in studies of long term illness. The Gerontologist, 4, 25–29.CrossRefGoogle ScholarPubMed
Teachman, J.D. (1983). Analyzing social processes; life tables and proportional hazards models. Social Science Research, 12, 263–301.CrossRefGoogle Scholar
Thompson, M.E., & Forbes, W.F. (1989). The problem of low response rates in surveys of the elderly. Mathematical Scientist, 14, 127–137.Google Scholar