Abstract
The dynamichazard package implements state space models that can provide a computationally efficient way to model time-varying parameters in survival analysis. I cover the models and some of the estimation methods implemented in dynamichazard, apply them to a large data set, and perform a simulation study to illustrate the methods' computation time and performance. One of the methods is compared with other models implemented in R which allow for left-truncation, right-censoring, time-varying covariates, and timevarying parameters.
Originalsprog | Engelsk |
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Tidsskrift | Journal of Statistical Software |
Vol/bind | 99 |
Udgave nummer | 7 |
Sider (fra-til) | 1-38 |
Antal sider | 38 |
DOI | |
Status | Udgivet - sep. 2021 |
Emneord
- Survival analysis
- Time-varying parameters
- Extended Kalman filter
- EM algorithm
- Unscented Kalman filter
- Parallel computing
- R
- Rcpp
- RcppArmadillo