Abstract
In this article, we propose a class of additive transformation models for recurrent event data, which includes the additive rates model as a special case. The new models offer great flexibility in formulating the effects of covariates on the mean function of recurrent events. Estimating equation approaches are developed for the model parameters, and asymptotic properties of the resulting estimators are established. In addition, a model checking procedure is presented to assess the adequacy of the model. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a bladder cancer study is presented.
| Original language | English |
|---|---|
| Pages (from-to) | 4043-4055 |
| Number of pages | 13 |
| Journal | Communications in Statistics - Theory and Methods |
| Volume | 42 |
| Issue number | 22 |
| DOIs | |
| State | Published - 17 Nov 2013 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Additive model
- Estimating equation
- Mean function
- Recurrent events
- Transformation model
Fingerprint
Dive into the research topics of 'Additive transformation models for recurrent events'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver