𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Dynamic Random Effects Models for Times Between Repeated Events

✍ Scribed by Daniel Y.T. Fong; K.F. Lam; J.F. Lawless; Y.W. Lee

Book ID
110314166
Publisher
Springer
Year
2001
Tongue
English
Weight
147 KB
Volume
7
Category
Article
ISSN
1380-7870

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

MIXTURE MODELS FOR THE JOINT DISTRIBUTIO
MIXTURE MODELS FOR THE JOINT DISTRIBUTION OF REPEATED MEASURES AND EVENT TIMES
✍ JOSEPH W. HOGAN; NAN M. LAIRD πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 548 KB

Many long-term clinical trials collect both a vector of repeated measurements and an event time on each subject; often, the two outcomes are dependent. One example is the use of surrogate markers to predict disease onset or survival. Another is longitudinal trials which have outcome-related dropout.

Random-effects Cox proportional hazards
Random-effects Cox proportional hazards model: General variance components methods for time-to-event data
✍ V. Shane Pankratz; Mariza de Andrade; Terry M. Therneau πŸ“‚ Article πŸ“… 2005 πŸ› John Wiley and Sons 🌐 English βš– 155 KB πŸ‘ 1 views

## Abstract Proportional hazards regression models are commonly used to study factors associated with time‐to‐event data. Because many complex genetic diseases exhibit variation in age at onset, it is important to have the capability to perform survival analyses on data collected from individuals w