A General Framework for Longitudinal Data through Marked Point Processes
作者:
Thomas H. Scheike,
期刊:
Biometrical Journal
(WILEY Available online 1997)
卷期:
Volume 39,
issue 1
页码: 57-67
ISSN:0323-3847
年代: 1997
DOI:10.1002/bimj.4710390107
出版商: WILEY‐VCH Verlag
关键词: Conditional least squares;Longitudinal data;Marked point process;Parametric inference;Repeated measure;Regression models;Weighted least squares
数据来源: WILEY
摘要:
AbstractThis paper reviews a general framework for the modelling of longitudinal data with random measurement times based on marked point processes and presents a worked example. We construct a quite general regression models for longitudinal data, which may in particular include censoring that only depend on the past and outside random variation, and dependencies between measurement times and measurements. The modelling also generalises statistical counting process models. We review a non‐parametric Nadarya‐Watson kernel estimator of the regression function, and a parametric analysis that is based on a conditional least squares (CLS) criterion. The parametric analysis presented, is a conditional version of the generalised estimation equations of LIANG and ZEGER (1986). We conclude that the usual nonparametric and parametric regression modelling can be applied to this general set‐up, with some modifications. The presented framework provides an easily implemented and powerful tool for model building for repeated measure
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