add_probs.survreg {ciTools}  R Documentation 
This function is one of the methods of add_probs
and is
automatically called when an object of class survreg
is
passed to add_probs
.
## S3 method for class 'survreg'
add_probs(
df,
fit,
q,
name = NULL,
yhatName = "median_pred",
comparison = "<",
confint = TRUE,
alpha = 0.05,
...
)
df 
A data frame of new data. 
fit 
An object of class 
q 
A double. A quantile of the survival time distribution. In survival applications this is the time of event. 
name 

yhatName 
A string. Name of the vector of predictions. 
comparison 
A character vector of length one. If

confint 
A logical. If 
alpha 
A number. Control the confidence level of the
confidence intervals if 
... 
Additional arguments. 
Confidence intervals may be produced for estimated probabilities of
accelerated failure time models. Presently, confidence intervals
may be computed for lognormal, weibull, exponential, and
loglogistic failure time models. If comparison = "<"
,
confidence intervals are made for the probability that a failure
will be observed before q
. Similarly, if comparison =
">"
, confidence intervals will be formed for the probability that
a unit fails after q
. In the survival literature,
comparison = ">"
corresponds to estimating the survivor
function, S(q).
Confidence intervals are produced parametrically via the Delta Method. Simulations show that under a mild to moderate amount of censoring, this method performs adequately.
The logistic transformation is applied to ensure
that confidence interval bounds lie between 0
and 1
.
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_probs.survreg
cannot inspect the convergence of
fit
. Poor maximum likelihood estimates will result in poor
confidence intervals. Inspect any warning messages given from
survreg
.
A dataframe, df
, with predicted medians, probabilities,
and confidence intervals for predicted probabilities attached.
For the logistic transformation of estimated probabilities and error bounds: Meeker, William Q., and Luis A. Escobar. Statistical methods for reliability data. John Wiley & Sons, 2014. (Chapter 8)
For a discussion of forming confidence intervals for survival probabilities: Harrell, Frank E. Regression modeling strategies. Springer, 2015. (Chapter 17)
add_ci.survreg
for confidence intervals for
survreg
objects, add_pi.survreg
for
prediction intervals of survreg
objects, and
add_quantile.survreg
for response quantiles of
survreg
objects.
## 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")
## Calculate the level 0.75 quantile wit CIs for that quantile
add_probs(df, fit, q = 500, name = c("Fhat", "lwr", "upr"))
## Try a weibull model for the same data:
fit2 < survival::survreg(survival::Surv(time, status) ~ age + I(age^2),
data = df, dist = "weibull")
## Calculate the level 0.75 quantile with CIs for the quantile
add_probs(df, fit2, q = 500, name = c("Fhat", "lwr", "upr"))