rmscb: Fitting Restricted Mean Survival Time Models with a...

In statapps/bhm: Biomarker Threshold Models

Fitting Restricted Mean Survival Time Models with a Continuous Biomarker

Description

{rmscb} is an R function for restricted mean survival time (RMST) as a continuous function for a biomarker variables. The model is specified by giving a symbolic description of the linear predictor and a description of the distribution family.

Usage

  rmscb(x, ...)

  ## S3 method for class 'formula'
rmscb(formula, data, subset, na.action, tau=5, h=0.2, w0=NULL, 
        sig.level = 0.95, rho = 2,...)
# use 
#          rmscb(y ~ biomarker)             
#
# to fit a prognostic model with biomarker term only 
# 
# use
#          rmscb(y ~ biomarker + trt)
#
# to fit the difference of RMSTs between two treatment groups.
#
# use 
   ## Default S3 method:
rmscb(x, y, control, ...)
#
# to fit a model without formula, where the biomarker shall be in the 
# first dependent variable

Arguments

formula	an object of class "formula"(or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
data	an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which "rmscb" is called.
tau	a prespecified time point at which the restricted mean survival time will be calculated.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the 'na.action' setting of 'options', and is 'na.fail' if that is unset. The 'factory-fresh' default is 'na.omit'. Another possible value is 'NULL', no action. Value 'na.exclude' can be useful.
h	the bandwidth, default is h = 0.2, if h = NULL, then the bandwidth will be selected using the cross validation method.
w0	the values of biomarker at which the RMST E(T|w=w0) will be estimated.
sig.level	the significant level of the simultaneuous confidence band will be constructed.
rho	the mode for the prediction error used in the cross validation bandwidth selection.
x	for 'rmscb.default', x is a design matrix of dimension n * p
y	y is a vector of observations of length n for a "Surv" survival object.
control	a list of parameters for controlling the fitting process. See "rmsControl" for details
...	additional arguments to be passed to the low level regression fitting functions (see below).

Details

'biomarker' is a Biomarker variable. This variable is required and shall be the first dependent variable in the formula.

'rmscb.default' is the workhorse functions: they are not normally called directly but can be more efficient where the response vector, design matrix and family have already been calculated.

Value

rmscb returns an object of class inheriting from "rmscb". See later in this section.

The function "summary" (i.e., "summary.rmscb") can be used to obtain or print a summary of the results, for example, the 95 percent CI of the parameters.

An object of class "rmscb" is a list containing at least the following components:

w0	w0 from the input.
rms	a named vector of restriected mean survival time from the "rmscb".
LB	lower bound of the simultaneuous confidence band.
UB	upper bound of the simultaneuous confidence band.

Author(s)

Wen Teng, Wenyu Jiang and Bingshu E. Chen ([e-mail])

References

Teng, W., Jiang, W. and Chen, B. E. (2022). Continuous threshold models with two-way interactions in survival analysis. Statistics in Medicine, submitted.

Examples

##
## Generate a random data set
n = 100
age = runif(n, 0, 1)*100
tm = rexp(n, 1/10)
status = rbinom(n, 1, 0.5)
trt= rbinom(n, 1, 0.5)

## fit a restricted mean survival time with one biomarker 

   fit = rmscb(Surv(tm, status)~age)
   print(fit)
## plot(fit)
## summary(fit) 

statapps/bhm documentation built on April 5, 2024, 3:31 a.m.