tram (version 1.1-2)

Survreg: Parametric Survival Models

Description

Weibull, log-normal, log-logistic and other parametric models (not exclusively) for survival analysis

Usage

Survreg(formula, data, subset, weights, offset, cluster, na.action = na.omit, 
        dist = c("weibull", "logistic", "gaussian", "exponential", "rayleigh", 
                 "loggaussian", "lognormal", "loglogistic"), scale = 0, ...)

Value

An object of class Survreg, with corresponding coef, vcov, logLik, estfun, summary, print, plot and predict methods.

Arguments

formula

an object of class "formula": a symbolic description of the model structure to be fitted. The details of model specification are given under tram and in the package vignette.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If present, the weighted log-likelihood is maximised.

offset

this can be used to specify an _a priori_ known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases.

cluster

optional factor with a cluster ID employed for computing clustered covariances.

na.action

a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset.

dist

character defining the conditional distribution of the (not necessarily positive) response, current choices include Weibull, logistic, normal, exponential, Rayleigh, log-normal (same as log-gaussian), or log-logistic.

scale

a fixed value for the scale parameter(s).

...

additional arguments to tram.

Details

Parametric survival models reusing the interface of survreg. The parameterisation is, however, a little different, see the package vignette.

The model is defined with a negative shift term. Large values of the linear predictor correspond to large values of the conditional expectation response (but this relationship is nonlinear). Parameters are log-hazard ratios comparing a reference with treatment (or a one unit increase in a numeric variable).

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110--134, tools:::Rd_expr_doi("10.1111/sjos.12291").

Examples

  data("GBSG2", package = "TH.data")

  library("survival")
  survreg(Surv(time, cens) ~ horTh, data = GBSG2)

  Survreg(Surv(time, cens) ~ horTh, data = GBSG2)