The regression to the mean effect has been described in several contexts but still it continues to emerge. It is a kind of selection bias and it is the consequence of measurement error. It was described in clinical studies and epidemiological investigations whenever a selection of high/low responders is part of the study design; second it is present when baseline measurement is considered as confounder of covariate of interests. The general setting of the problem can be formalized via a latent variable and more than one imperfect measurements. Generalized linear mixed models are proposed. We present a comprehensive formulation of the problem and a simple explorative analysis using the correlation coefficients (Pearson’s, Lin’s and Bland-Altman’s mean-difference correlation).
Regression to the mean / Annibale Biggeri; Michela Baccini; Dolores Catelan. - ELETTRONICO. - (2013), pp. 0-0. (Intervento presentato al convegno SIS2013 Statistical Conference).
Regression to the mean
BIGGERI, ANNIBALE;CATELAN, DOLORES
2013
Abstract
The regression to the mean effect has been described in several contexts but still it continues to emerge. It is a kind of selection bias and it is the consequence of measurement error. It was described in clinical studies and epidemiological investigations whenever a selection of high/low responders is part of the study design; second it is present when baseline measurement is considered as confounder of covariate of interests. The general setting of the problem can be formalized via a latent variable and more than one imperfect measurements. Generalized linear mixed models are proposed. We present a comprehensive formulation of the problem and a simple explorative analysis using the correlation coefficients (Pearson’s, Lin’s and Bland-Altman’s mean-difference correlation).
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