simulation_frailty: Simulated data incorporating shared frailty effects to model...
In survregVB: Variational Bayesian Analysis of Survival Data
simulation_frailty R Documentation
Simulated data incorporating shared frailty effects to model clustered
time-to-event data.
Description
Simulated data incorporating shared frailty effects to model clustered
time-to-event data.
Usage
simulation_frailty
Format
A dataframe with 75 observations grouped into 15 clusters, each
with 5 individuals.
- x1
Continuous covariate from N(1, 0.2^2)
- x2
Binary covariate from Bernoulli(0.5)
- Time
True survival time
- Time.15
Observed survival time accounting for uniformly distributed
right censoring time from uniform(0,u)
- delta
Event indicator for uncensored data (always 1 in this
simulation.)
- delta.15
Event indicator after censoring (1 = event, 0 =
censored).
- cluster
Cluster ID (1–15), indicating group-level frailty
.
@references Xian, C., Souza, C. P. E. de, He, W., Rodrigues, F. F.,
& Tian, R. (2024). Fast variational bayesian inference for correlated
survival data: An application to invasive mechanical ventilation
duration analysis. https://doi.org/10.48550/ARXIV.2408.00177
survregVB documentation built on June 8, 2025, 1:46 p.m.
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| simulation_frailty | R Documentation |
Simulated data incorporating shared frailty effects to model clustered time-to-event data.
Description
Simulated data incorporating shared frailty effects to model clustered time-to-event data.
Usage
simulation_frailty
Format
A dataframe with 75 observations grouped into 15 clusters, each with 5 individuals.
- x1
Continuous covariate from
N(1, 0.2^2)- x2
Binary covariate from
Bernoulli(0.5)- Time
True survival time
- Time.15
Observed survival time accounting for uniformly distributed right censoring time from
uniform(0,u)- delta
Event indicator for uncensored data (always 1 in this simulation.)
- delta.15
Event indicator after censoring (1 = event, 0 = censored).
- cluster
Cluster ID (1–15), indicating group-level frailty
. @references Xian, C., Souza, C. P. E. de, He, W., Rodrigues, F. F., & Tian, R. (2024). Fast variational bayesian inference for correlated survival data: An application to invasive mechanical ventilation duration analysis. https://doi.org/10.48550/ARXIV.2408.00177
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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