RANDOM-EFFECTS MODELS FOR LONGITUDINAL DATA USING GIBBS SAMPLING

被引：62
作者：
GILKS, WR
WANG, CC
YVONNET, B
COURSAGET, P
机构：
[1] FDA,CBER,DIV BIOSTAT & EPIDEMIOL,BETHESDA,MD
[2] FAC PHARM TOURS,INST VIROL,TOURS,FRANCE
[3] FAC PHARM TOURS,MICROBIOL LAB,TOURS,FRANCE
来源：
BIOMETRICS | 1993年 / 49卷 / 02期
关键词：
BAYESIAN INFERENCE; CENSORED DATA; CONVERGENCE; GIBBS SAMPLING; HEPATITIS-B IMMUNIZATION; HETEROGENEITY; LONGITUDINAL DATA; RANDOM EFFECTS; VARIANCE COMPONENTS;
D O I：
10.2307/2532557
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Analysis of longitudinal studies is often complicated through differences amongst individuals in the number and spacing of observations. Laird and Ware (1982, Biometrics 38, 963-974) proposed a linear random-effects model to deal with this problem. We propose a generalisation of this model to accommodate multiple random effects, and show how Gibbs sampling can be used to estimate it. We illustrate the methodology with an analysis of long-term response to hepatitis B vaccination, and demonstrate that the methodology can be easily and effectively extended to deal with censoring in the dependent variable.
引用
收藏
页码：441 / 453
页数：13
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