eha (version 2.4-5)

coxreg: 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(), weights, subset,
t.offset, 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, hazards = 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.

weights

Case weights; time-fixed or time-varying.

subset

An optional vector specifying a subset of observations to be used in the fitting process.

t.offset

Case offsets; time-varying.

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

Logical. If center = TRUE (default), the baseline hazards are calculated at the means of the covariates and for the reference category for factors, otherwise at the value zero. See Details.

x

Return the design matrix in the model object?

y

return the response in the model object?

hazards

Calculate baseline hazards? Default is TRUE.

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.

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, calculated at the means of the covariates (rather, columns of the design matrix). Is a list, with one component per stratum. Each component is a matrix with two columns, the first contains risktimes, the second the corresponding hazard atom.

means

Means of the columns of the design matrix corresponding to covariates, if center = TRUE. Columns corresponding to factor levels gice a zero in the corresponding position in means. If center = FALSE, means are all zero.

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.

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.

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, discrete-model, based.

If center = TRUE (default), graphs show the "baseline" distribution at the means of (continuous) covariates, and for the reference category in case of factors (avoiding representing "flying pigs"). If center = FALSE the baseline distribution is at the value zero of all covariates. It is usually a good idea to use center = FALSE in combination with "precentering" of covariates, that is, subtracting a reference value, ideally close to the center of the covariate distribution. In that way, the "reference" will be the same for all subsets of the data.

References

Brostr<U+00F6>m, 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