Abstract
We incorporate a random clustering effect into the nonparametric version of Cox Proportional Hazards model to characterize clustered survival data. The simulation studies provide evidence that clustered survival data can be better characterized through a nonparametric model. Predictive accuracy of the nonparametric model is affected by number of clusters and distribution of the random component accounting for clustering effect. As the functional form of the covariate departs from linearity, the nonparametric model is becoming more advantageous over the parametric counterpart. Finally, nonparametric is better than parametric model when data are highly heterogenous and/or there is misspecification error.
Original language | English (US) |
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Pages (from-to) | 603-618 |
Number of pages | 16 |
Journal | Communications in Statistics: Simulation and Computation |
Volume | 46 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2 2017 |
Bibliographical note
Publisher Copyright:© 2017 Taylor & Francis Group, LLC.
Keywords
- Backfitting algorithm
- Clustered data
- Generalized additive models
- Nonparametric regression
- Random effects
- Survival analysis
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation