add_ci.survreg {ciTools}  R Documentation 
Confidence Intervals for the Mean Survival Time of Accelerated Failure Time Models.
Description
This function is one of the methods for add_ci
, and is
called automatically when add_ci
is used on a fit
of
class survreg
.
Usage
## S3 method for class 'survreg'
add_ci(df, fit, alpha = 0.1, names = NULL, yhatName = "mean_pred", ...)
Arguments
df 
A data frame of new data on which to form predictions and confidence intervals. 
fit 
An object of class 
alpha 
A number between 0 and 1. 1  
names 

yhatName 
A string. Name of the vector of predictions. The
default name is 
... 
Additional arguments. 
Details
add_ci.survreg
calculates confidence intervals for the mean
survival time of several accelerated failure time (AFT) models
including exponential, lognormal, weibull, and loglogistic
models. AFT models must be fit with the survreg
function in
the survival
package. Confidence intervals are formed
parametrically via the Delta method.
add_ci.survreg
will compute confidence intervals for the
following mean survival time point estimates:
Exponential: E[YX] = \exp{X\beta}
Weibull: E[YX] = \exp{X\beta}\Gamma(1 + \sigma)
Lognormal: E[YX] = \exp{X\beta + \frac{\sigma^2}{2}}
Loglogistic: E[YX] = \exp{X\beta}\Gamma(1 + \sigma)(1  \sigma)
Traditionally, survival time predictions are made with the median
survival time. For forming confidence intervals for the median
survival time (or any quantile of the survival time distribution),
see add_quantile.survreg
.
Note: The expected survival time of a loglogistic model with scale
>= 1 does not exist. Otherwise, expected survival times exist for
each of the four AFT models considered in add.ci_survreg
.
Note: Due to a limitation, the Surv
object must be specified in
survreg
function call. See the examples section for one way
to do this.
Note: add_ci.survreg
cannot inspect the convergence of
fit
. Poor maximum likelihood estimates will result in poor
confidence intervals. Inspect any warning messages given from
survreg
.
Value
A dataframe, df
, with predicted expected values and
level 1  alpha level confidence levels attached.
References
For descriptions of the loglocation scale models supported: Meeker, William Q., and Luis A. Escobar. Statistical methods for reliability data. John Wiley & Sons, 2014. (Chapter 4)
For a description of the multivariate Delta method: Meeker, William Q., and Luis A. Escobar. Statistical methods for reliability data. John Wiley & Sons, 2014. (Appendix B.2)
See Also
add_quantile.survreg
for quantiles of the
survival time distribution of survreg
objects,
add_pi.survreg
for prediction intervals of
survreg
objects, and add_probs.survreg
for
survival probabilities of survreg
objects.
Examples
## Define a data set.
df < survival::stanford2
## remove a covariate with missing values.
df < df[, 1:4]
## next, create the Surv object inside the survreg call:
fit < survival::survreg(survival::Surv(time, status) ~ age + I(age^2),
data = df, dist = "lognormal")
add_ci(df, fit, alpha = 0.1, names = c("lwr", "upr"))
## Try a different model:
fit2 < survival::survreg(survival::Surv(time, status) ~ age + I(age^2),
data = df, dist = "weibull")
add_ci(df, fit2, alpha = 0.1, names = c("lwr", "upr"))