Marginal Regression Models for Clustered Ordinal Measurements
作者:
PatrickJ. Heagerty,
ScottL. Zeger,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 435
页码: 1024-1036
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476973
出版商: Taylor & Francis Group
关键词: Estimating equation;Global odds ratio;Proportional odds model
数据来源: Taylor
摘要:
This article constructs statistical models for clustered ordinal measurements. We specify two regression models: one for the marginal means and one for the marginal pairwise global odds ratios. Of particular interest are problems in which the odds ratio regression is a focus. Simple assumptions about higher-order conditional moments give a quadratic exponential likelihood function with second-order estimating equations (GEE2) as score equations. But computational difficulty can arise for large clusters when both the mean response and the association between measures is of interest. First, we present GEE1 as an alternative estimation strategy. Second, we extend to repeated ordinal measurements the method developed by Carey et al. for binary observations that is based on alternating logistic regressions (ALR) for the marginal mean parameters and the pairwise log-odds ratio parameters. We study the efficiency of GEE1 and ALR relative to full maximum likelihood. We demonstrate the utility of our regression methods for ordinal data by applying the methods to a surgical follow-up study.
点击下载:
PDF (2052KB)
返 回