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Effect of regression to the mean in the presence of within‐subject variability
Author(s) -
Johnson William D.,
George Varghese T.
Publication year - 1991
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780100812
Subject(s) - statistics , regression toward the mean , regression , regression analysis , replicate , observational error , regression diagnostic , econometrics , mathematics , regression dilution , subject (documents) , linear regression , computer science , polynomial regression , library science
Regression to the mean arises often in statistical applications where the units chosen for study relate to some observed characteristic in the extreme of its distribution. Gardner and Heady attribute the effect of regression to the mean to measurement errors. They assume the model Y i = U + e i , where U is a fixed within‐subject component and e i is the random measurement error. They suggest several replicate measurements to reduce the regression effect under the assumption that the measurement errors e i are independent within subjects. While measurement errors play an important role in regression to the mean, one should not overlook within‐subject variation. In this paper, we consider a model to estimate the regression effect in the presence of correlated within‐subject effects as well as independent measurement errors.