aftreg: Accelerated Failure Time Regression

In eha: Event History Analysis

Accelerated Failure Time Regression

Description

The accelerated failure time model with parametric baseline hazard(s). Allows for stratification with different scale and shape in each stratum, and left truncated and right censored data.

Usage

aftreg(
  formula = formula(data),
  data = parent.frame(),
  na.action = getOption("na.action"),
  dist = "weibull",
  init,
  shape = 0,
  id,
  param = c("lifeAcc", "lifeExp"),
  control = list(eps = 1e-08, maxiter = 20, trace = FALSE),
  singular.ok = TRUE,
  model = FALSE,
  x = FALSE,
  y = 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.
na.action	a missing-data filter function, applied to the model.frame, after any subset argument has been used. Default is options()$na.action.
dist	Which distribution? Default is "weibull", with the alternatives "gompertz", "ev", "loglogistic" and "lognormal". A special case like the exponential can be obtained by choosing "weibull" in combination with shape = 1.
init	vector of initial values of the iteration. Default initial value is zero for all variables.
shape	If positive, the shape parameter is fixed at that value. If zero or negative, the shape parameter is estimated. Stratification is now regarded as a meaningful option even if shape is fixed.
id	If there are more than one spell per individual, it is essential to keep spells together by the id argument. This allows for time-varying covariates.
param	Which parametrization should be used? The lifeAcc uses the parametrization given in the vignette, while the lifeExp uses the same as in the survreg function.
control	a list with components eps (convergence criterion), maxiter (maximum number of iterations), and trace (logical, debug output if TRUE). You can change any component without mention the other(s).
singular.ok	Not used.
model	Not used.
x	Return the design matrix in the model object?
y	Return the response in the model object?

Details

The parameterization is different from the one used by survreg, when param = "lifeAcc". The result is then true acceleration of time. Then the model is

S(t; a, b, \beta, z) = S_0((t / \exp(b - z\beta))^{\exp(a)})

where S_0 is some standardized survivor function. The baseline parameters a and b are log shape and log scale, respectively. This is for the default parametrization. With the lifeExp parametrization, some signs are changed:

b - z beta

is changed to

b + z beta

. For the Gompertz distribution, the base parametrization is canonical, a necessity for consistency with the shape/scale paradigm (this is new in 2.3).

Value

A list of class "aftreg" 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.
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).
n.events	Number of events in data.
terms	Used by extractor functions.
assign	Used by extractor functions.
wald.test	The Wald 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.
call	The call.
method	The method.
convergence	Did the optimization converge?
fail	Did the optimization fail? (Is NULL if not).
pfixed	TRUE if shape was fixed in the estimation.
param	The parametrization.

Author(s)

Göran Broström

See Also

coxreg, phreg, survreg

Examples

data(mort)
aftreg(Surv(enter, exit, event) ~ ses, param = "lifeExp", data = mort)

eha documentation built on Oct. 7, 2024, 1:17 a.m.