WinBUGS code for the unrelated mean effects model

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 "OR", "MD", "SMD", or "ROM" for the odds ratio, mean difference, standardised mean difference and ratio of means, respectively.
model: 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".
assumption: 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.
connected: An integer equal to one or larger that indicates the number of subnetworks.

Value

An R character vector object to be passed to run_ume through the textConnection function as the argument object.

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.

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

Author

Loukia M. Spineli