prepare_model {rnmamod}

WinBUGS code for Bayesian pairwise or network meta-analysis and meta-regression

Description

The WinBUGS code, as written by Dias et al. (2013) to run a one-stage Bayesian network meta-analysis, extended to incorporate the pattern-mixture model for binary or continuous missing participant outcome data (Spineli et al., 2021; Spineli, 2019). The model has been also extended to incorporate a trial-level covariate to apply meta-regression (Cooper et al., 2009). In the case of two interventions, the code boils down to a one-stage Bayesian pairwise meta-analysis with pattern-mixture model (Turner et al., 2015; Spineli et al, 2021).

Usage

prepare_model(measure, model, covar_assumption, assumption)

Arguments

measure

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.

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".

covar_assumption

Character string indicating the structure of the intervention-by-covariate interaction, as described in Cooper et al., (2009). Set covar_assumption equal to one of the following, when meta-regression is performed: "exchangeable", "independent", and "common". Assign "NO" to perform pairwise or network meta-analysis.

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.

Details

prepare_model creates the model in the JAGS dialect of the BUGS language. The output of this function constitutes the argument model.file of the jags function (in the R-package R2jags) via the textConnection function.

Value

An R character vector object to be passed to run_model and run_metareg through the textConnection function as the argument object.

Author(s)

Loukia M. Spineli

References

Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med 2009;28(14):1861–81. doi: 10.1002/sim.3594

Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making 2013;33(5):607–17. doi: 10.1177/0272989X12458724

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

Turner NL, Dias S, Ades AE, Welton NJ. A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis. Stat Med 2015;34(12):2062–80. doi: 10.1002/sim.6475

See Also

run_metareg, run_model, jags, textConnection