eha (version 1.2-18)

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, 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, 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.

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

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.

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.

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.

References

Brostr�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