aft: Parametric accelerated failure time model with smooth time...

In rstpm2: Smooth Survival Models, Including Generalized Survival Models

Parametric accelerated failure time model with smooth time functions

Description

This implements the accelerated failure time models S_0(t exp(beta x)) and S_0(int_0^t exp(beta x(u)) du). The baseline function S_0(t*) is modelled as exp(-exp(eta_0(log(t*)))), where eta_0(log(t*)) is a linear predictor using natural splines.

Usage

aft(formula, data, smooth.formula = NULL, df = 3,
    tvc = NULL, cure.formula = ~1, control = list(),
    init = NULL, weights = NULL, tvc.intercept = TRUE,
    tvc.integrated = FALSE,
    timeVar = "", time0Var = "",
    cure = FALSE, mixture = FALSE, contrasts = NULL, subset = NULL, ...)

Arguments

formula	a formula object, with the response on the left of a ~ operator, and the regression terms (excluding time) on the right. The response should be a survival object as returned by the Surv function. The terms can include linear effects for any time-varying coefficients. [required]
data	a data-frame in which to interpret the variables named in the formula argument. [at present: required]
smooth.formula	a formula for describing the time effects for the linear predictor, excluding the baseline S_0(t*), but including time-dependent acceleration factors. The time-dependent acceleration factors can be modelled with any smooth functions.
df	an integer that describes the degrees of freedom for the ns function for modelling the baseline log-cumulative hazards function (default=3).
tvc	a list with the names of the time-varying coefficients. This uses natural splines (e.g. tvc=list(hormon=3) is equivalent to smooth.formula=~...+hormon:nsx(log(time),df=3)), which by default does not include an intercept (or main effect) term.
cure.formula	a formula for describing the cure fraction.
control	control argument passed to optim.
init	init should either be FALSE, such that initial values will be determined using Cox regression, or a numeric vector of initial values.
weights	an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
tvc.intercept	logical for whether to include an intercept in the time-varying acceleration factor (defaults to TRUE)
tvc.integrated	logical for whether the time-varying acceleration factor should be based on a integration, rather than a cumulative effect (defaults to FALSE)
timeVar	string variable defining the time variable. By default, this is determined from the survival object, however this may be ambiguous if two variables define the time.
time0Var	string variable to determine the entry variable; useful for when more than one data variable is used in the entry time.
cure	logical for whether to model for cure using a non-mixture model (default=FALSE)
mixture	logical for whether to model for cure using a mixture model (default=FALSE)
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
...	additional arguments to be passed to the mle2.

Details

The implementation extends the mle2 object from the bbmle package. The model inherits all of the methods from the mle2 class.

Value

An aft-class object that inherits from mle2-class.

Author(s)

Mark Clements.

See Also

survreg, coxph

Examples

summary(aft(Surv(rectime,censrec==1)~hormon,data=brcancer,df=4))

rstpm2 documentation built on Oct. 29, 2024, 5:06 p.m.