The WinBUGS code, as written by Dias et al. (2010) to run a one-stage Bayesian node-splitting model, extended to incorporate the pattern-mixture model for binary or continuous missing participant outcome data (Spineli et al., 2021; Spineli, 2019).
prepare_nodesplit(measure, model, assumption)An R character vector object to be passed to
run_nodesplit through the
textConnection function as the argument
object.
Character string indicating the effect measure. For a binary
outcome, the following can be considered: "OR", "RR" or
"RD" for the odds ratio, relative risk, and risk difference,
respectively. For a continuous outcome, the following can be considered:
"MD", "SMD", or "ROM" for mean difference,
standardised mean difference and ratio of means, respectively.
Character string indicating the analysis model with values
"RE", or "FE" for the random-effects and fixed-effect model,
respectively. The default argument is "RE".
Character string indicating the structure of the
informative missingness parameter. Set assumption equal to one of
the following: "HIE-COMMON", "HIE-TRIAL", "HIE-ARM",
"IDE-COMMON", "IDE-TRIAL", "IDE-ARM",
"IND-CORR", or "IND-UNCORR". The default argument is
"IDE-ARM". The abbreviations "IDE", "HIE", and
"IND" stand for identical, hierarchical and independent,
respectively. "CORR" and "UNCORR" stand for correlated and
uncorrelated, respectively.
Loukia M. Spineli
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_nodesplit 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_nodesplit
currently considers the WinBUGS code for the odds ratio.
The split nodes have been automatically selected via the
mtc.nodesplit.comparisons
function of the R-package
gemtc.
See 'Details' in run_nodesplit.
Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med 2010;29(7-8):932--44. doi: 10.1002/sim.3767
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
jags,
run_model,
mtc.nodesplit.comparisons,
run_nodesplit,
textConnection