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August 1997 Random effects analysis of children's blood pressure data
Daniel Rabinowitz, Steven Shea
Statist. Sci. 12(3): 185-194 (August 1997). DOI: 10.1214/ss/1030037908


Some analyses of longitudinal blood pressure data have focused on the question of whether a current value of blood pressure is predictive of subsequent rate of change. A positive correlation between blood pressure values at the beginning of a longitudinal study and rate of change over the course of the study has been found in studies of adults. Negative correlation, however, has been found in a study of children. These studies, either implicitly or explicitly, rely on linear growth curve models in which subjects' blood pressure observations are assumed to follow simple linear regression models with slopes and intercepts varying among subjects, but with the slopes constant over time.

Our analysis of a longitudinal data set of 2,203 measurements of systolic blood pressure from 216 children also provided a negative estimate of the correlation. However, smoothed plots of cross products of residuals suggested that an alternative random effects model, in which rate of change of systolic blood pressure is not treated as constant over time, might better fit the data. It is possible that the negative estimates of the correlation found in children's blood pressure data are an artifact of assuming a constant rate of change when the data actually follow the alternative model. It is shown that the expected result of fitting the linear growth curve model to data that follow the alternative model is an apparent negative correlation between slope and intercept. In the data, the observed estimates of the parameters of the linear growth curve model are consistent with the observed estimates of the parameters of the alternative model.


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Daniel Rabinowitz. Steven Shea. "Random effects analysis of children's blood pressure data." Statist. Sci. 12 (3) 185 - 194, August 1997.


Published: August 1997
First available in Project Euclid: 22 August 2002

Digital Object Identifier: 10.1214/ss/1030037908

Keywords: autoregressive , horse-racing effect , Jenss effect , linear growth curve , Ornstein-Uhlenbeck process , random effects , regression dilution bias

Rights: Copyright © 1997 Institute of Mathematical Statistics


Vol.12 • No. 3 • August 1997
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