Inference for variance components in linear mixed-effect models with flexible random effect and error distributions.

Statistical Methods in Medical Research
Tom Chen, Rui Wang

Abstract

In many biomedical investigations, parameters of interest, such as the intraclass correlation coefficient, are functions of higher-order moments reflecting finer distributional characteristics. One popular method to make inference for such parameters is through postulating a parametric random effects model. We relax the standard normality assumptions for both the random effects and errors through the use of the Fleishman distribution, a flexible four-parameter distribution which accounts for the third and fourth cumulants. We propose a Fleishman bootstrap method to construct confidence intervals for correlated data and develop a normality test for the random effect and error distributions. Recognizing that the intraclass correlation coefficient may be heavily influenced by a few extreme observations, we propose a modified, quantile-normalized intraclass correlation coefficient. We evaluate our methods in simulation studies and apply these methods to the Childhood Adenotonsillectomy Trial sleep electroencephalogram data in quantifying wave-frequency correlation among different channels.

References

Jun 1, 1957·Annals of Human Genetics·C A SMITH
Sep 21, 2004·British Journal of Anaesthesia·O DresslerE F Kochs
Dec 21, 2004·Biometrics·Wendimagegn GhideyPaul Eilers
Oct 13, 2005·Statistics in Medicine·Tsung I Lin, Jack C Lee
Nov 27, 2007·Statistics in Medicine·Lei Liu, Zhangsheng Yu
May 23, 2013·The New England Journal of Medicine·Carole L MarcusUNKNOWN Childhood Adenotonsillectomy Trial (CHAT)
Apr 21, 2016·Clinical Trials : Journal of the Society for Clinical Trials·Shuang HuangMelanie L Bell

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Software Mentioned

REML
GEE2
R
QN
CHAT
REMM
ICC

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