survreg2weibull | R Documentation |
Transforms output from survreg
using the Weibull distribution
to a more natural parameterization. See details for more information.
survreg2weibull(model, conf.level = 0.95)
model |
A |
conf.level |
Significance level used to produce two-sided
|
The survreg
function fits a Weibull accelerated failure time
model of the form
\log t = \mu + \gamma^T Z + \sigma W,
where Z
is a matrix of covariates, and W
has the extreme value
distribution, \mu
is the intercept, \gamma
is a vector of
parameters for each of the covariates, and \sigma
is the scale. The
usual parameterization of the model, however, is defined by hazard function
h(t|Z) = \alpha \lambda t^{\alpha - 1} \exp(\beta^T Z).
The transformation is as follows: \alpha = 1/\sigma
, \lambda =
\exp(-\mu/\sigma)
, and \beta=-\gamma/\sigma
, and estimates of the
standard errors can be found using the delta method.
The Weibull distribution has the advantage of having two separate
interpretations. The first, via proportional hazards, leads to a hazard
ratio, defined by \exp \beta
. The second, of accelerated failure
times, leads to an event time ratio (also known as an acceleration factor),
defined by \exp (-\beta/\alpha)
.
Further details regarding the transformations of the parameters and their
standard errors can be found in Klein and Moeschberger (2003, Chapter 12).
An explanation of event time ratios for the accelerated failure time
interpretation of the model can be found in Carroll (2003). A general
overview can be found in the vignette("weibull")
of this package.
vars |
A matrix containing the values of the transformed parameters and their standard errors |
HR |
A matrix containing the
hazard ratios for the covariates, and |
ETR |
A matrix containing the event time ratios for the
covariates, and |
Sarah R. Haile
Carroll, K. (2003). On the use and utility of the Weibull model in the analysis of survival data. Controlled Clinical Trials, 24, 682–701.
Klein, J. and Moeschberger, M. (2003). Survival analysis: techniques for censored and truncated data. Springer.
Requires the packages survival and prodlim. This
function is used by weibullReg
.
data(larynx)
survreg2weibull(survreg(Surv(time, death) ~ stage + age, data = larynx),
conf.level = 0.95)
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