Περίληψη:
Several methods for the estimation and comparison of rates of change in
longitudinal Studies with staggered entry and informative drop-outs have
been recently proposed. For multivariate normal linear models, REML
estimation is used. There are various approaches to maximizing the
corresponding log-likelihood; in this paper we use a restricted
iterative generalized least squares method (RIGLS) combined with a
nested EM algorithm. An important statistical problem in such approaches
is the estimation of the standard errors adjusted for the missing data
(observed data information matrix). Louis has provided a general
technique for computing the observed data information in terms of
completed data quantities within the EM framework. The multiple
imputation (MI) method for obtaining variances can be regarded as an
alternative to this. The aim of this paper is to develop, apply and
compare the Louis and a modified MI method in the setting of
longitudinal studies where the source of missing data is either death or
disease progression (informative) or end of the study (assumed
non-informative). Longitudinal data are simultaneously modelled with the
missingness process. The methods are illustrated by modelling CD4 count
data from an HIV-1 clinical trial and evaluated through simulation
studies. Both methods, Louis and MI, are used with Monte Carlo
simulations of the missing data using the appropriate conditional
distributions, the former with 100 simulations, the latter with 5 and
10. It is seen that naive SEs based on the completed data likelihood can
be seriously biased. This bias was largely corrected by Louis and
modified MI methods, which gave broadly similar estimates. Given the
relative simplicity of the modified MI method, it may be preferable.
Copyright (C) 2003 John Wiley Sons, Ltd.
Συγγραφείς:
Touloumi, G
Babiker, AG
Kenward, MG
Pocock, SJ and
Darbyshire, JH
