apply_mcmc | R Documentation |
Fit a dynamic borrowing Weibull survival model to the given dataset and extract the posterior
samples using MCMC.
See the user guide for more information on the model formulation.
See run_mcmc()
for more information on the available parameters for tuning the MCMC sampling
process
apply_mcmc(dt, formula_cov, ...) extract_samples(object) ## S3 method for class 'apply_mcmc' summary(object, ...)
dt |
A data.frame containing data required for modelling. See details |
formula_cov |
A one sided formula specifying which non-treatment covariates should be included into the model. See details |
... |
Additional arguments passed onto |
object |
A |
apply_mcmc()
The dt
data.frame must contain 1 row per subject with the following variables:
time - A continuous non-zero number specifying the time that the subject had an event at
cnsr - A column of 0/1's where 1 indicates that the event was censored/right truncated
ext - A column of 0/1's where 1 indicates that the subject was part of the external control
trt - A column of 0/1's where 1 indicates that the subject was receiving the experimental treatment
The dt
data.frame may also contain any additional covariates to be used in the Weibull model
as specified by formula_cov
. In order to fit a valid model formula_cov
must contain
the intercept term. The formula will be automatically adjusted to include the treatment term
and as such should not be included here, if you want to include a treatment interaction term
this should be done by using ~ trt:covariate
and NOT via ~ trt*covariate
.
extract_samples()
This function can be used to extract the samples generated by apply_mcmc()
summary()
This function provides summary statistics about the samples generated by apply_mcmc()
The extracted samples can be roughly defined as follows (see the user guide for full details):
HR_cc_hc
- The hazard ratio between the concurrent control arm and the historical
control arm. This can be
be thought of as the ratio of the scale parameter between the baseline trial distribution and
the baseline
external control distribution. This is equivalent to exp(alpha[2] - alpha[1])
HR_trt_cc
- The hazard ratio between the treatment arm and the concurrent control arm.
This is equivalent
to exp(beta_trt)
alpha[1]
- The shape parameter for the trial's baseline distribution
alpha[2]
- The shape parameter for the historical control's baseline distribution
beta_trt
- The log-hazard ratio for the treatment effect. This is equivalent to
log(HR_trt_cc)
beta_<var>
- The log-hazard ratio for any other covariate provided to the model via
formula_cov
r0
- The scale parameter for the baseline distribution of both the trial and the
historical control
tau/sigma
- The precision/variance for alpha[1]
i.e. controls how much
information is borrowed from the
historical control arm
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