coxtp {surtvep} | R Documentation |
fit a Cox non-proportional hazards model with P-spline or Smoothing-spline, with penalization tuning parameter chosen by information criteria or cross-validation
Description
Fit a Cox non-proportional hazards model via penalized maximum likelihood.
Usage
coxtp(
event,
z,
time,
strata = NULL,
penalty = "Smooth-spline",
nsplines = 8,
lambda = c(0.1, 1, 10),
degree = 3L,
knots = NULL,
ties = "Breslow",
tol = 1e-06,
iter.max = 20L,
method = "ProxN",
gamma = 1e+08,
btr = "dynamic",
tau = 0.5,
stop = "ratch",
parallel = FALSE,
threads = 2L,
fixedstep = FALSE
)
Arguments
event |
failure event response variable of length |
z |
input covariate matrix, with |
time |
observed event times, which should be a vector with non-negative values. |
strata |
a vector of indicators for stratification.
Default = |
penalty |
a character string specifying the spline term for the penalized Newton method.
This term is added to the log-partial likelihood, and the penalized log-partial likelihood serves as the new objective function to
control the smoothness of the time-varying coefficients.
Default is
If |
nsplines |
number of basis functions in the splines to span the time-varying effects. The default value is 8.
We use the R function |
lambda |
a user-specified |
degree |
degree of the piecewise polynomial for generating the B-spline basis functions—default is 3 for cubic splines.
If the If the |
knots |
the internal knot locations (breakpoints) that define the B-splines.
The number of the internal knots should be |
ties |
a character string specifying the method for tie handling. If there are no tied events,
the methods are equivalent.
By default |
tol |
tolerance used for stopping the algorithm. See details in |
iter.max |
maximum iteration number if the stopping criterion specified by |
method |
a character string specifying whether to use Newton method or proximal Newton method. If |
gamma |
parameter for proximal Newton method |
btr |
a character string specifying the backtracking line-search approach. |
tau |
a positive scalar used to control the step size inside the backtracking line-search. The default value is 0.5. |
stop |
a character string specifying the stopping rule to determine convergence.
|
parallel |
if |
threads |
an integer indicating the number of threads to be used for parallel computation. The default value is |
fixedstep |
if |
Details
The sequence of models implied by lambda.spline
is fit by the (proximal) Newton method.
The objective function is
loglik - P_{\lambda},
where P_{\lambda}
is a penalty matrix for P-spline
or Smooth-spline
.
The \lambda
is the tuning parameter (See details in lambda
). Users can define the initial sequence.
The function IC
below provides different information criteria to choose the tuning parameter \lambda
. Another function cv.coxtp
uses the cross-validation to choose the tuning parameter.
Value
A list of objects with S3 class "coxtp"
. The length is the same as that of lambda
; each represents the model output with each value of the tuning parameter lambda
.
call |
the call that produced this object. |
beta |
the estimated time-varying coefficient for each predictor at each unique time.
It is a matrix of dimension |
bases |
the basis matrix used in model fitting. If |
ctrl.pts |
estimated coefficient of the basis matrix of dimension |
Hessian |
the Hessian matrix of the log-partial likelihood, of which the dimension is |
internal.knots |
the internal knot locations (breakpoints) that define the B-splines. |
nobs |
number of observations. |
penalty |
the spline term |
theta.list |
the history of |
VarianceMatrix |
the variance matrix of the estimated coefficients of the basis matrix, which is the inverse of the negative Hessian matrix. |
References
Boyd, S., and Vandenberghe, L. (2004) Convex optimization.
Cambridge University Press.
Gray, R. J. (1992) Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis.
Journal of the American Statistical Association, 87(420): 942-951.
Gray, R. J. (1994) Spline-based tests in survival analysis.
Biometrics, 50(3): 640-652.
Luo, L., He, K., Wu, W., and Taylor, J. M. (2023) Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.
Statistical Methods in Medical Research, in press.
Perperoglou, A., le Cessie, S., and van Houwelingen, H. C. (2006) A fast routine for fitting Cox models with time varying effects of the covariates.
Computer Methods and Programs in Biomedicine, 81(2): 154-161.
Wu, W., Taylor, J. M., Brouwer, A. F., Luo, L., Kang, J., Jiang, H., and He, K. (2022) Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.
Lifetime Data Analysis, 28(2): 194-218.
Wood, S. N. (2017) P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data.
Statistics and Computing, 27(4): 985-989.
See Also
IC
, cv.coxtp
plot
, get.tvcoef
and baseline
.
Examples
data(ExampleData)
z <- ExampleData$z
time <- ExampleData$time
event <- ExampleData$event
lambda = c(0,1)
fit <- coxtp(event = event, z = z, time = time, lambda=lambda)