Comparison of baseline and repeated measure covariate techniques in the Framingham Heart Study.

The purpose of this paper is to indicate how repeated measures on risk factors have been employed in the prediction of the development of disease in the Framingham Heart Study. Since these measures vary over time, the method accounts for time dependent covariates. The technique is a generalized person-years approach in that it treats each observation interval (of equal length) as a mini-follow-up study in which the current risk factor measurements are employed to predict an event in the interval. Observations over multiple intervals are pooled into a single sample to predict the short term risk of an event. This approach is compared to the long-term prediction of disease which utilizes only the baseline measurements and ignores subsequent repeated measures on the risk factors.

[1]  K Y Liang,et al.  Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.

[2]  R. Abbott Logistic regression in survival analysis. , 1985, American journal of epidemiology.

[3]  J. Kalbfleisch,et al.  The Analysis of Panel Data under a Markov Assumption , 1985 .

[4]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[5]  T. Dawber,et al.  The Framingham Study: The Epidemiology of Atherosclerotic Disease , 1980 .

[6]  A. Hofman,et al.  Does change in blood pressure predict heart disease? , 1983, British medical journal.

[7]  M J Symons,et al.  A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. , 1983, Journal of chronic diseases.

[8]  R. Prentice,et al.  Regression analysis of grouped survival data with application to breast cancer data. , 1978, Biometrics.

[9]  J. Ware,et al.  On the use of repeated measurements in regression analysis with dichotomous responses. , 1979, Biometrics.

[10]  N. Cook,et al.  Design and analysis methods for longitudinal research. , 1983, Annual review of public health.

[11]  D. Cox,et al.  Analysis of Survival Data. , 1985 .

[12]  M. Woodbury,et al.  A dynamic analysis of chronic disease development: a study of sex specific changes in coronary heart disease incidence and risk factors in Framingham. , 1981, International journal of epidemiology.

[13]  A. V. Peterson,et al.  Serial blood pressure measurements and cardiovascular disease in a Japanese cohort. , 1982, American journal of epidemiology.

[14]  A S Whittemore,et al.  Methods for analyzing panel studies of acute health effects of air pollution. , 1979, Biometrics.

[15]  G. Berry Longitudinal observations. Their usefulness and limitations with special reference to the forced expiratory volume. , 1974, Bulletin de physio-pathologie respiratoire.

[16]  David R. Cox The analysis of binary data , 1970 .

[17]  G G Koch,et al.  A general methodology for the analysis of experiments with repeated measurement of categorical data. , 1977, Biometrics.

[18]  T. Dawber,et al.  The development of coronary heart disease in relation to sequential biennial measures of cholesterol in the Framingham study. , 1966, Journal of chronic diseases.

[19]  W. Hauck A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. , 1985, Journal of Chronic Diseases.

[20]  M. Woodbury,et al.  Longitudinal analysis of the dynamics and risk of coronary heart disease in the Framingham Study. , 1979, Biometrics.

[21]  D. Cox Regression Models and Life-Tables , 1972 .

[22]  G. Little,et al.  The natural history of chronic bronchitis and emphysema , 1979 .

[23]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[24]  T Heeren,et al.  Sudden death in the Framingham Heart Study. Differences in incidence and risk factors by sex and coronary disease status. , 1984, American journal of epidemiology.