coxreg: Cox regression

In goranbrostrom/eha: Event History Analysis

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 = NULL, x =
FALSE, y = TRUE, hazards = NULL, boot = FALSE, efrac = 0, geometric = FALSE,
rs = NULL, frailty = NULL, max.survs = NULL, coxph = TRUE)

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	deprecated. See Details.
x	Return the design matrix in the model object?
y	return the response in the model object?
hazards	deprecated. Was: Calculate baseline hazards? Default is TRUE. Calculating hazards is better done separately, after fitting. In most cases.
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.
rs	Risk set?
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.
coxph	Logical, defaults to TRUE. Determines if standard work should be passed to coxph via entry points.

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.

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 component is the maximized value.
score	The score test statistic (at the initial value).
linear.predictors	The estimated linear predictors.
residuals	The martingale residuals.
hazards	The estimated baseline hazards, calculated at the value zero 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 risk times, 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).
n.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.
call	The call.
method	The method.
n.strata	Number of strata.
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
 

goranbrostrom/eha documentation built on June 19, 2024, 1:39 a.m.