An application of isotonic longitudinal marginal regression to monitoring the healing process

被引：7

作者：

Fahrmeir, L ^{[1
]}

Gieger, C ^{[1
]}

Heumann, C ^{[1
]}

机构：

[1] Univ Munich, Inst Stat, D-80539 Munich, Germany

来源：

BIOMETRICS | 1999年 / 55卷 / 03期

关键词：

isotonic ordinal repeated measurements; iterative proportional fitting; marginal regression; nonparametric predictors; penalized generalized estimating equations;

D O I：

10.1111/j.0006-341X.1999.00951.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper discusses marginal regression for repeated ordinal measurements that are isotonic over time. Such data are often observed in longitudinal studies on healing processes in which, due to recovery, the status of patients only improves or remains the same. We show how this prior information can be used to construct appropriate and parsimoniously parametrized marginal models. As a second aspect, we also incorporate nonparametric fitting of covariate effects via a penalized quasi-likelihood or general estimating equation approach. We illustrate our methods by an application to sports-related injuries.

引用

页码：951 / 956

页数：6

共 17 条

[1]

Agresti A., 1990, Analysis of categorical data

[2] Modeling repeated measures with monotonic ordinal responses and misclassification, with applications to studying maturation [J].