survregVB.frailty.fit {survregVB} | R Documentation |
Variational Bayesian Analysis of Correlated Survival Data Using a Log-Logistic Accelerated Failure Time Model
Description
Called by survregVB
to do the actual parameter and ELBO computations
for correlated survival data with shared frailty (a random intercept).
This routine does no checking that the arguments are the proper length
or type.
Usage
survregVB.frailty.fit(
Y,
X,
alpha_0,
omega_0,
mu_0,
v_0,
lambda_0,
eta_0,
cluster,
max_iteration = 100,
threshold = 1e-04
)
Arguments
Y |
A |
X |
A design matrix including covariates with first column of ones to represent the intercept. |
alpha_0 |
The shape hyperparameter |
omega_0 |
The shape hyperparameter |
mu_0 |
Hyperparameter |
v_0 |
The precision (inverse variance) hyperparameter |
lambda_0 |
The shape hyperparameter |
eta_0 |
The scale hyperparameter |
cluster |
An optional variable which clusters the observations to introduce shared frailty for correlated survival data. |
max_iteration |
The maximum number of iterations for the variational inference optimization. If reached, iteration stops. (Default:100) |
threshold |
The convergence threshold for the evidence based lower bound (ELBO) optimization. If the difference between the current and previous ELBO's is smaller than this threshold, iteration stops. (Default:0.0001) |
Details
Implements the Variational Bayes algorithm with random intercepts proposed in the paper "Fast variational bayesian inference for correlated survival data: An application to invasive mechanical ventilation duration analysis".
For right-censored survival time T_{ij}
of the j_{th}
subject
from the i_{th}
cluster in the sample, in a sample, i=1,...,K
and j=1,...,n_i
, the shared-frailty log-logistic AFT model is specified
as follows:
\log(T_{ij})=\gamma_i+X_{ij}^T\beta+b\epsilon_{ij}
, where
-
X_{ij}
is a column vector of lengthp, p\ge2
containingp-1
covariates and a constant one to incorporate the intercept (i.e.,X_i=(1,x_{ij1},...,x_{ij(p-1)})^T
), -
\beta
is the corresponding vector of coefficients for the fixed effects, -
\gamma_i
is a random intercept for thei_{th}
cluster, -
\epsilon_{ij}
is a random variable following a standard logistic distribution, and -
b is a scale parameter.
Value
A list containing results of the fit.
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
See Also
Examples
fit <- survregVB.frailty.fit(
X = matrix(c(rep(1, 75), simulation_frailty$x1, simulation_frailty$x2), nrow = 75),
Y = survival::Surv(simulation_frailty$Time, simulation_frailty$delta),
alpha_0 = 3,
omega_0 = 2,
mu_0 = c(0, 0, 0),
v_0 = 0.1,
lambda_0 = 3,
eta_0 = 2,
cluster = simulation_frailty$cluster
)