Abstract
The longitudinal data analysis generally involves the special challenges methodologically with censoring and repeated observations. A subject is followed longitudinally over time and change is recorded in status of the event. In longitudinal studies, generally data on time to occurrence of events may be either complete or incomplete. The partially incomplete data pose special type of challenge to statistical modeling and which has been a focus of research for a long time. In Chapter 16, multistate and multistage hazards models are described, estimation and test procedures are shown for repeated measures data. The techniques are illustrated with examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Islam, M.A., Chowdhury, R.I. (2017). Multistate and Multistage Models. In: Analysis of Repeated Measures Data. Springer, Singapore. https://doi.org/10.1007/978-981-10-3794-8_15
Download citation
DOI: https://doi.org/10.1007/978-981-10-3794-8_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3793-1
Online ISBN: 978-981-10-3794-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)