splineCox.reg2 {splineCox} | R Documentation |
Fitting the five-parameter spline Cox model with a specified shape, selecting the best fit
Description
splineCox.reg2
estimates the parameters of a five-parameter spline Cox model for multiple specified shapes
and selects the best-fitting model based on the maximization of the log-likelihood function.
This function supports predefined model shapes and custom numeric vectors of length 5.
If numeric vectors are provided, they will be normalized to have an L1 norm of 1.
Additionally, if plot = TRUE
, the function generates a plot of the estimated baseline hazard function for the best-fitting model,
along with its 95
The x-axis represents time, and the y-axis represents the estimated hazard.
The solid line indicates the estimated hazard function, while the dashed red lines represent the confidence intervals.
Usage
splineCox.reg2(
t.event,
event,
Z,
xi1 = min(t.event),
xi3 = max(t.event),
model = names(shape.list),
p0 = rep(0, 1 + ncol(as.matrix(Z))),
plot = TRUE
)
Arguments
t.event |
a vector for time-to-event |
event |
a vector for event indicator (=1 event; =0 censoring) |
Z |
a matrix for covariates; nrow(Z)=sample size, ncol(Z)=the number of covariates |
xi1 |
lower bound for the hazard function; the default is |
xi3 |
upper bound for the hazard function; the default is |
model |
A list of character strings and/or numeric vectors of length 5 specifying the shapes of the baseline hazard function to evaluate.
Character options include:
"increase", "constant", "decrease", "unimodal1", "unimodal2", "unimodal3", "bathtub1", "bathtub2", "bathtub3".
Numeric vectors must be of length 5 and will be normalized to have an L1 norm of 1.
Default is |
p0 |
Initial values to maximize the likelihood (1 + p parameters; baseline hazard scale parameter and p regression coefficients) |
plot |
A logical value indicating whether to plot the estimated baseline hazard function.
If |
Value
A list containing the following components:
model |
A character string indicating the shape of the baseline hazard function used. |
parameter |
A numeric vector of the parameters defining the baseline hazard shape. |
beta |
A named vector with the estimates, standard errors, and 95% confidence intervals for the regression coefficients. |
gamma |
A named vector with the estimate, standard error, and 95% confidence interval for the baseline hazard parameter. |
loglik |
A named vector containing the log-likelihood ( |
other_models |
A data frame containing the log-likelihood ( |
plot |
A baseline hazard function plot for the best-fitting model (if |
References
Teranishi, R.; Furukawa, K.; Emura, T. (2025). A Two-Stage Estimation Approach to Cox Regression Under the Five-Parameter Spline Model Mathematics 13(4), 616. doi:10.3390/math13040616 Available at https://www.mdpi.com/2227-7390/13/4/616
Examples
# Example data
library(joint.Cox)
data(dataOvarian)
t.event = dataOvarian$t.event
event = dataOvarian$event
Z = dataOvarian$CXCL12
M = c("constant", "increase", "decrease")
reg2 <- splineCox.reg2(t.event, event, Z, model = M)
print(reg2)