Optimal designs for empirical bayes estimators of individual linear and quadratic growth curves in linear mixed models.
Many studies on optimal designs for linear mixed model analysis of repeated measures data have focussed on estimating the fixed effects. The present study investigates the optimal number of time points and subjects in case random effects have to be estimated. Linear mixed models with a linear or quadratic trend across equidistant time points are studied. Given a particular cost function, we examine which designs minimise the expected average squared prediction error. Robustness of the optimal design, important when one does not know the underlying model, is also treated.
Candel
Math Jjm
MJ
Department of Methodology and Statistics, Maastricht University, The Netherlands. Math.Candel@Stat.unimaas.nl
D000929:Antidepressive Agents, Tricyclic / Q000008:administration & dosage; D001499:Bayes Theorem; D003863:Depression / Q000188:drug therapy; D006801:Humans; D007099:Imipramine / Q000008:administration & dosage; D016014:Linear Models; D012107:Research Design / Q000706:statistics & numerical data*; D016896:Treatment Outcome
D000929:Antidepressive Agents, Tricyclic; D007099:Imipramine