A General Framework for Longitudinal Data through Marked Point Processes
✍ Scribed by Thomas H. Scheike
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 560 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0323-3847
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✦ Synopsis
This 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 pahcular 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 nonparametric Nadarya-Watson kernel estimator of the regression function, and a parametric analysis that is based on a conditional least squares (CIS) criterion. The panmetric 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 measurements.