RT Journal Article
T1 A generalized Q–Q plot for longitudinal data
A1 Pardo Llorente, María del Carmen
A1 Alonso Sanz, Rosa
AB Most biomedical research is carried out using longitudinal studies. The method of generalized estimating equations (GEEs) introduced by Liang and Zeger [Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13-22] and Zeger and Liang [Longitudinal data analysis for discrete and continuous outcomes, Biometrics 42 (1986), pp. 121-130] has become a standard method for analyzing non-normal longitudinal data. Since then, a large variety of GEEs have been proposed. However, the model diagnostic problem has not been explored intensively. Oh et al. [Modeldiagnostic plots for repeated measures data using the generalized estimating equations approach, Comput. Statist. Data Anal. 53 (2008), pp. 222-232] proposed residual plots based on the quantile-quantile (Q-Q) plots of the chi(2)-distribution for repeated-measures data using the GEE methodology. They considered the Pearson, Anscombe and deviance residuals. In this work, we propose to extend this graphical diagnostic using a generalized residual. A simulation study is presented as well as two examples illustrating the proposed generalized Q-Q plots.
PB Routledge Journals, Taylor & Francis
SN 0266-4763
YR 2012
FD 2012
LK https://hdl.handle.net/20.500.14352/42441
UL https://hdl.handle.net/20.500.14352/42441
LA eng
DS Docta Complutense
RD 7 may 2024