Data in survival analysis are usually assumed to be univariate, with one, possibly censored, lifetime for each individual. All the standard methodology, including Kaplan-Meier plots and Cox analysis, is geared toward handling this situation. However, multivariate survival data also arise naturally in many contexts. Such data pose a problem for ordinary multivariate methods, which will have difficulty handling censored data.
There are two typical ways multivariate survival data can arise. One, which may be termed the recurrent events situation, is when several successive events of the same type are registered for each individual, for instance, repeated occurrences of ear infections. The other, which may be termed the clustered survival data situation, is when several units that may fail are collected in a cluster. Examples of the clustered survival data situation may be the possible failure of several dental fillings for an individual or the lifetimes of twins. The cluster structure may in fact be rather complex, including, for instance, related individuals in family groups. Sometimes one would assume common distributions for the individual units in a cluster; in other cases, like when considering the lifetimes of father and son, the distributions may be different.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2008). Multivariate frailty models. In: Survival and Event History Analysis. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68560-1_7
Download citation
DOI: https://doi.org/10.1007/978-0-387-68560-1_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-20287-7
Online ISBN: 978-0-387-68560-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)