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.
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, ...)An aft-class object that inherits from mle2-class.
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]
a data-frame in which to interpret the variables named in the
formula argument. [at present: required]
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.
an integer that describes the degrees of freedom for the ns
function for modelling the baseline log-cumulative hazards function (default=3).
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.
a formula for describing the cure fraction.
control argument passed to optim.
init should either be FALSE, such that initial
values will be determined using Cox regression, or a numeric
vector of initial values.
an optional vector of 'prior weights' to be used in the
fitting process. Should be NULL or a numeric vector.
logical for whether to include an intercept in the time-varying acceleration factor (defaults to TRUE)
logical for whether the time-varying acceleration factor should be based on a integration, rather than a cumulative effect (defaults to FALSE)
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.
string variable to determine the entry variable; useful for when more than one data variable is used in the entry time.
logical for whether to model for cure using a non-mixture model (default=FALSE)
logical for whether to model for cure using a mixture model (default=FALSE)
an optional list. See the contrasts.arg
of model.matrix.default.
an optional vector specifying a subset of observations to be used in the fitting process.
additional arguments to be passed to the mle2.
Mark Clements.
The implementation extends the mle2 object from the
bbmle package. The model inherits all of the methods from the
mle2 class.
survreg, coxph
summary(aft(Surv(rectime,censrec==1)~hormon,data=brcancer,df=4))
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