Fits proportional hazards model with some effects time-varying and some effects constant. Time dependent variables and counting process data (multiple events per subject) are possible.
timecox(
formula = formula(data),
data,
weights,
subset,
na.action,
start.time = 0,
max.time = NULL,
id = NULL,
clusters = NULL,
n.sim = 1000,
residuals = 0,
robust = 1,
Nit = 20,
bandwidth = 0.5,
method = "basic",
weighted.test = 0,
degree = 1,
covariance = 0
)a formula object with the response on the left of a '~' operator, and the independent terms on the right as regressors. The response must be a survival object as returned by the `Surv' function. Time-invariant regressors are specified by the wrapper const(), and cluster variables (for computing robust variances) by the wrapper cluster().
a data.frame with the variables.
for analysis
to subset
to have na.action
start of observation period where estimates are computed.
end of observation period where estimates are computed. Estimates thus computed from [start.time, max.time]. Default is max of data.
For timevarying covariates the variable must associate each record with the id of a subject.
cluster variable for computation of robust variances.
number of simulations in resampling.
to returns residuals that can be used for model validation in the function cum.residuals
to compute robust variances and construct processes for resampling. May be set to 0 to save memory.
number of iterations for score equations.
bandwidth for local iterations. Default is 50 % of the range of the considered observation period.
Method for estimation. This refers to different parametrisations of the baseline of the model. Options are "basic" where the baseline is written as \(\lambda_0(t) = \exp(\alpha_0(t))\) or the "breslow" version where the baseline is parametrised as \(\lambda_0(t)\).
to compute a variance weighted version of the test-processes used for testing time-varying effects.
gives the degree of the local linear smoothing, that is local smoothing. Possible values are 1 or 2.
to compute covariance estimates for nonparametric terms rather than just the variances.
Returns an object of type "timecox". With the following arguments:
cumulative timevarying regression coefficient estimates are computed within the estimation interval.
the martingale based pointwise variance estimates.
robust pointwise variances estimates.
estimate of parametric components of model.
variance for gamma.
robust variance for gamma.
list with residuals. Estimated martingale increments (dM) and corresponding time vector (time).
observed absolute value of supremum of cumulative components scaled with the variance.
p-value for covariate effects based on supremum test.
resampled supremum values.
observed absolute value of supremum of difference between observed cumulative process and estimate under null of constant effect.
p-value based on resampling.
resampled supremum values.
observed integrated squared differences between observed cumulative and estimate under null of constant effect.
p-value based on resampling.
resampled supremum values.
resampling based constant to construct robust 95% uniform confidence bands.
observed test-process of difference between observed cumulative process and estimate under null of constant effect over time.
list of 50 random realizations of test-processes under null based on resampling.
Schoenfeld residuals are returned for "breslow" parametrisation.
Resampling is used for computing p-values for tests of timevarying effects.
The modelling formula uses the standard survival modelling given in the survival package.
The data for a subject is presented as multiple rows or 'observations', each of which applies to an interval of observation (start, stop]. When counting process data with the )start,stop] notation is used, the 'id' variable is needed to identify the records for each subject. The program assumes that there are no ties, and if such are present random noise is added to break the ties.
Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).
# NOT RUN {
data(sTRACE)
# Fits time-varying Cox model
out<-timecox(Surv(time/365,status==9)~age+sex+diabetes+chf+vf,
data=sTRACE,max.time=7,n.sim=100)
summary(out)
par(mfrow=c(2,3))
plot(out)
par(mfrow=c(2,3))
plot(out,score=TRUE)
# Fits semi-parametric time-varying Cox model
out<-timecox(Surv(time/365,status==9)~const(age)+const(sex)+
const(diabetes)+chf+vf,data=sTRACE,max.time=7,n.sim=100)
summary(out)
par(mfrow=c(2,3))
plot(out)
# }
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