coxreg {eha}R Documentation

Cox regression

Description

Performs Cox regression with some special attractions, especially sampling of risksets and the weird bootstrap.

Usage

coxreg(formula = formula(data), data = parent.frame(),
na.action = getOption("na.action"), init = NULL,
method = c("efron", "breslow", "mppl", "ml"),
control = list(eps = 1e-08, maxiter = 25, trace = FALSE),
singular.ok = TRUE, model = FALSE,
center = TRUE,
x = FALSE, y = TRUE, boot = FALSE, efrac = 0,
geometric = FALSE, rs = NULL,
frailty = NULL, max.survs = NULL)

Arguments

formula a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
data a data.frame in which to interpret the variables named in the formula.
na.action a missing-data filter function, applied to the model.frame, after any subset argument has been used. Default is options()$na.action.
init vector of initial values of the iteration. Default initial value is zero for all variables.
method Method of treating ties, "efron" (default), "breslow", "mppl" (maximum partial partial likelihood), or "ml" (maximum likelihood).
control a list with components eps (convergence criterion), maxiter (maximum number of iterations), and silent (logical, controlling amount of output). You can change any component without mention the other(s).
singular.ok Not used
model Not used
center If TRUE, the hazards are calculated at the means of the covariates. If FALSE, at zero.
x Return the design matrix in the model object?
y return the response in the model object?
rs Risk set?
boot Number of boot replicates. Defaults to FALSE, no boot samples.
efrac Upper limit of fraction failures in 'mppl'.
geometric If TRUE, forces an 'ml' model with constant riskset probability. Default is FALSE.
frailty Grouping variable for frailty analysis. Not in use yet.
max.survs Sampling of risk sets? If given, it should be (the upper limit of) the number of survivors in each risk set.

Details

The default method, efron, and the alternative, breslow, are both the same as in coxph in package survival. The methods mppl and ml are maximum likelihood based.

Value

A list of class c("coxreg", "coxph") with components

coefficients Fitted parameter estimates.
var Covariance matrix of the estimates.
loglik Vector of length two; first component is the value at the initial parameter values, the second componet is the maximized value.
score The score test statistic (at the initial value).
linear.predictors The estimated linear predictors.
residuals The martingale residuals.
hazard The estimated baseline hazard.
means Means of the columns of the design matrix.
w.means Weighted (against exposure time) means of covariates; weighted relative frequencies of levels of factors.
n Number of spells in indata (possibly after removal of cases with NA's).
events Number of events in data.
terms Used by extractor functions.
assign Used by extractor functions.
wald.test The Walt test statistic (at the initial value).
y The Surv vector.
isF Logical vector indicating the covariates that are factors.
covars The covariates.
ttr Total Time at Risk.
levels List of levels of factors.
formula The calling formula.
bootstrap The (matrix of) bootstrap replicates, if requested on input. It is up to the user to do whatever desirable with this sample.
boot.sd The estimated standard errors of the bootstrap replicates.
call The call.
method The method.
convergence Did the optimization converge?
fail Did the optimization fail? (Is NULL if not).

Warning

The use of rs is dangerous, see note. It can however speed up computing time considerably for huge data sets.

Note

This function starts by creating risksets, if no riskset is supplied via rs, with the aid of risksets. Supplying output from risksets via rs fails if there are any NA's in the data! Note also that it depends on stratification, so rs contains information about stratification. Giving another strata variable in the formula is an error. The same is ok, for instance to supply stratum interactions.

Author(s)

Göran Broström

References

Broström, G. and Lindkvist, M. (2008). Partial partial likelihood. Communications in Statistics: Simulation and Computation 37:4, 679-686.

See Also

coxph, risksets

Examples


 dat <- data.frame(time=  c(4, 3,1,1,2,2,3),
                status=c(1,1,1,0,1,1,0),
                x=     c(0, 2,1,1,1,0,0),
                sex=   c(0, 0,0,0,1,1,1))
 coxreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
 # Same as:
 rs <- risksets(Surv(dat$time, dat$status), strata = dat$sex)
 coxreg( Surv(time, status) ~ x, data = dat, rs = rs) #stratified model
 

[Package eha version 1.2-4 Index]