id: 05278600
dt: j
an: 1135.62053
au: Shen, Lei; Shao, Jun; Park, Soomin; Palta, Mari
ti: Between- and within-cluster covariate effects and model misspecification in
    the analysis of clustered data.
so: Stat. Sin. 18, No. 2, 731-748 (2008).
py: 2008
pu: Academia Sinica, Institute of Statistical Science, Taipei
la: EN
cc: 62H30 62G08
ut: attenuation; confounding; cohort effects; generalized estimating equations;
    linear mixed effects models; measurement error; period effects; body
    mass index; aging; formaldehyde emission; perceived sleepiness
ci: 
li: http://www3.stat.sinica.edu.tw/statistica/J18N2/J18N217/J18N217.html
ab: Summary: We consider the analysis of clustered data using linear mixed
    effects models and generalized estimating equations, where covariates
    can be decomposed into between- and within-cluster components. Under
    the false assumption of equal between- and within- cluster covariate
    effects, we simultaneously study the asymptotic behavior of the
    estimators for regression coefficients, intra-cluster correlation and
    residual error variance. This provides a more complete assessment of
    the effect of such model misspecification than is currently available
    in the literature. We then apply the results to gain insights into the
    effects of confounding and measurement error. Key findings include the
    structure of bias when both cohort and period effect confounding are
    present, quantification of the attenuation effect of measurement error,
    effects of measurement error of some covariates on the estimation of
    coefficients of error-free covariates, and consistent estimation in the
    presence of measurement errors. The results are extended to allow
    different cluster sizes, and three longitudinal data sets are used for
    illustrative purposes.
rv: