prepare_ume {rnmamod} | R Documentation |
WinBUGS code for the unrelated mean effects model
Description
The WinBUGS code, as proposed by Dias et al. (2013) to run a one-stage Bayesian unrelated mean effects model, refined (Spineli, 2021), and extended to incorporate the pattern-mixture model for binary or continuous missing participant outcome data (Spineli et al., 2021; Spineli, 2019).
Usage
prepare_ume(measure, model, assumption, connected)
Arguments
measure |
Character string indicating the effect measure with values
|
model |
Character string indicating the analysis model with values
|
assumption |
Character string indicating the structure of the
informative missingness parameter. Set |
connected |
An integer equal to one or larger that indicates the number of subnetworks. |
Details
This functions creates the model in the JAGS dialect of the BUGS
language. The output of this function constitutes the argument
model.file
of jags
(in the R-package
R2jags) via the
textConnection
function.
prepare_ume
inherits measure
, model
, and
assumption
from the run_model
function. For a binary
outcome, when measure
is "RR" (relative risk) or "RD"
(risk difference) in run_model
, prepare_ume
currently considers the WinBUGS code for the odds ratio.
Value
An R character vector object to be passed to run_ume
through the textConnection
function as
the argument object
.
Author(s)
Loukia M. Spineli
References
Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making 2013;33(5):641–56. doi: 10.1177/0272989X12455847
Spineli LM. A revised framework to evaluate the consistency assumption globally in a network of interventions. Med Decis Making 2021. doi: 10.1177/0272989X211068005
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome data in network meta-analysis: a one-stage pattern-mixture model approach. Stat Methods Med Res 2021;30(4):958–75. doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol 2019;19(1):86. doi: 10.1186/s12874-019-0731-y
See Also
jags
, run_model
,
run_ume
, textConnection