TY - JOUR
T1 - Penalized likelihood estimation for semiparametric mixed models, withapplication to alcohol treatment research
AU - Chen, Jinsong
AU - Liu, Lei
AU - Johnson, Bankole A.
AU - O'Quigley, John
PY - 2013/1/30
Y1 - 2013/1/30
N2 - In this article, we implement a practical computational method for various semiparametric mixed effects models, estimating nonlinear functions by penalized splines. We approximate the integration of the penalized likelihood with respect to random effects with the use of adaptive Gaussian quadrature, which we can conveniently implement in SAS procedure NLMIXED. We carry out the selection of smoothing parameters through approximated generalized cross-validation scores. Our method has two advantages: (1) the estimation is more accurate than the current available quasi-likelihood method for sparse data, for example, binary data; and (2) it can be used in fitting more sophisticated models. We show the performance of our approach in simulation studies with longitudinal outcomes from three settings: binary, normal data after Box-Cox transformation, and count data with log-Gamma random effects. We also develop an estimation method for a longitudinal two-part nonparametric random effects model and apply it to analyze repeated measures of semicontinuous daily drinking records in a randomized controlled trial of topiramate.
AB - In this article, we implement a practical computational method for various semiparametric mixed effects models, estimating nonlinear functions by penalized splines. We approximate the integration of the penalized likelihood with respect to random effects with the use of adaptive Gaussian quadrature, which we can conveniently implement in SAS procedure NLMIXED. We carry out the selection of smoothing parameters through approximated generalized cross-validation scores. Our method has two advantages: (1) the estimation is more accurate than the current available quasi-likelihood method for sparse data, for example, binary data; and (2) it can be used in fitting more sophisticated models. We show the performance of our approach in simulation studies with longitudinal outcomes from three settings: binary, normal data after Box-Cox transformation, and count data with log-Gamma random effects. We also develop an estimation method for a longitudinal two-part nonparametric random effects model and apply it to analyze repeated measures of semicontinuous daily drinking records in a randomized controlled trial of topiramate.
KW - Generalized linear mixed models (GLMMs)
KW - Laplace approximation
KW - Logistic models
KW - Longitudinal data analysis
KW - Non-normal random effects
UR - http://www.scopus.com/inward/record.url?scp=84871271097&partnerID=8YFLogxK
U2 - 10.1002/sim.5528
DO - 10.1002/sim.5528
M3 - Article
C2 - 22833388
AN - SCOPUS:84871271097
SN - 0277-6715
VL - 32
SP - 335
EP - 346
JO - Statistics in medicine
JF - Statistics in medicine
IS - 2
ER -