Selection of covariance patterns for longitudinal data in semi-parametric models.
The use of patterned covariance structures in the parametric analysis of longitudinal data is both elegant and efficient. However, this strategy has not been well studied for semi-parametric models for analysing such data. We propose to estimate the covariance matrix in the semi-parametric model by rearranging the non-parametric component as a profiled linear function of the data and using a local smoothing technique. This results in a parametric regression formulation that enables us to construct likelihood functions and use various information criteria to select the best fitting covariance matrix. We apply our method to reanalyse data from a two-armed clinical trial for Scleroderma patients and show our method is more efficient for estimating the parametric components in the semi-parametric model.
Li
Jialiang
J
Department of Statistics and Applied Probability, National University of Singapore and Duke-NUS Graduate Medical School, Singapore. stalj@nus.edu.sg
Wong
Weng Kee
WK
D000704:Analysis of Variance; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D045743:Scleroderma, Diffuse / Q000188:drug therapy