set_agd_surv | R Documentation |
Set up a network containing aggregate survival data (AgD) in the form of
event/censoring times (e.g. reconstructed from digitized Kaplan-Meier curves)
and covariate summary statistics from each study. Multiple data sources may be
combined once created using combine_network()
.
set_agd_surv(
data,
study,
trt,
Surv,
covariates = NULL,
trt_ref = NULL,
trt_class = NULL
)
data |
a data frame |
study |
column of |
trt |
column of |
Surv |
column of |
covariates |
data frame of covariate summary statistics for each study
or study arm, with corresponding |
trt_ref |
reference treatment for the network, as a single integer, string, or factor. If not specified, a reasonable well-connected default will be chosen (see details). |
trt_class |
column of |
By default, trt_ref = NULL
and a network reference treatment will be chosen
that attempts to maximise computational efficiency and stability. If an
alternative reference treatment is chosen and the model runs slowly or has
low effective sample size (ESS) this may be the cause - try letting the
default reference treatment be used instead. Regardless of which treatment is
used as the network reference at the model fitting stage, results can be
transformed afterwards: see the trt_ref
argument of
relative_effects()
and predict.stan_nma()
.
All arguments specifying columns of data
accept the following:
A column name as a character string, e.g. study = "studyc"
A bare column name, e.g. study = studyc
dplyr::mutate()
style semantics for inline variable transformations, e.g. study = paste(author, year)
An object of class nma_data
set_ipd()
for individual patient data, set_agd_contrast()
for
contrast-based aggregate data, and combine_network()
for combining
several data sources in one network.
print.nma_data()
for the print method displaying details of the
network, and plot.nma_data()
for network plots.
## Newly diagnosed multiple myeloma
head(ndmm_agd) # Reconstructed Kaplan-Meier data
ndmm_agd_covs # Summary covariate information on each arm
set_agd_surv(ndmm_agd,
study = studyf,
trt = trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs)
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