run_ume {rnmamod}

Perform the unrelated mean effects model

Description

Performs the unrelated mean effects model of Dias et al. (2013) that has been refined (Spineli, 2021) and extended to address aggregate binary and continuous missing participant outcome data via the pattern-mixture model (Spineli et al. 2021; Spineli, 2019). This model offers a global evaluation of the plausibility of the consistency assumption in the network.

Usage

run_ume(full, n_iter, n_burnin, n_chains, n_thin, inits = NULL)

Arguments

full

An object of S3 class run_model. See 'Value' in run_model.

n_iter

Positive integer specifying the number of Markov chains for the MCMC sampling; an argument of the jags function of the R-package R2jags. The default argument is 10000.

n_burnin

Positive integer specifying the number of iterations to discard at the beginning of the MCMC sampling; an argument of the jags function of the R-package R2jags. The default argument is 1000.

n_chains

Positive integer specifying the number of chains for the MCMC sampling; an argument of the jags function of the R-package R2jags. The default argument is 2.

n_thin

Positive integer specifying the thinning rate for the MCMC sampling; an argument of the jags function of the R-package R2jags. The default argument is 1.

inits

A list with the initial values for the parameters; an argument of the jags function of the R-package R2jags. The default argument is NULL, and JAGS generates the initial values.

Details

run_ume inherits the arguments data, measure, model, assumption, heter_prior, mean_misspar, var_misspar, and ref from run_model. This prevents specifying a different Bayesian model from that considered in run_model.Therefore, the user needs first to apply run_model, and then use run_ume (see 'Examples').

The run_ume function also returns the arguments data, model, measure, assumption, n_chains, n_iter, n_burnin, and n_thin as specified by the user to be inherited by other relevant functions of the package.

Initially, run_ume calls the improved_ume function to identify the frail comparisons, that is, comparisons between non-baseline interventions in multi-arm trials not investigated in any two-arm or multi-arm trial of the network (Spineli, 2021). The 'original' model of Dias et al. (2013) omits the frail comparisons from the estimation process. Consequently, the number of estimated summary effects is less than those obtained by performing separate pairwise meta-analyses (see run_series_meta).

For a binary outcome, when measure is "RR" (relative risk) or "RD" (risk difference) in run_model, run_ume currently considers the odds ratio as effect measure for being the base-case effect measure in run_model for a binary outcome (see also 'Details' in run_model).

run_ume calls the prepare_ume function which contains the WinBUGS code as written by Dias et al. (2013) for binomial and normal likelihood to analyse binary and continuous outcome data, respectively. prepare_ume has been extended to incorporate the pattern-mixture model with informative missingness parameters for binary and continuous outcome data (see 'Details' in run_model). prepare_ume has also been refined to account for the multi-arm trials by assigning conditional univariate normal distributions on the underlying trial-specific effect size of comparisons with the baseline arm of the multi-arm trial (Spineli, 2021).

run_ume runs Bayesian unrelated mean effects model in JAGS. The progress of the simulation appears on the R console. The model is updated until convergence using the autojags function of the R-package R2jags with 2 updates and number of iterations and thinning equal to n_iter and n_thin, respectively.

The output of run_ume is not end-user-ready. The ume_plot function uses the output of run_ume as an S3 object and processes it further to provide an end-user-ready output.

run_ume can be used only for a network of interventions. In the case of two interventions, the execution of the function will be stopped and an error message will be printed on the R console.

Value

An R2jags output on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) of the following monitored parameters:

EM

The summary effect estimate (according to the argument measure defined in run_model) for each pairwise comparison observed in the network.

dev_o

The deviance contribution of each trial-arm based on the observed outcome.

hat_par

The fitted outcome at each trial-arm.

tau

The between-trial standard deviation (assumed common across the observed pairwise comparisons) for the whole network, when a random-effects model has been specified.

m_tau

The between-trial standard deviation (assumed common across the observed pairwise comparisons) for the subset of multi-arm trials, when a random-effects model has been specified.

The output also includes the following elements:

leverage_o

The leverage for the observed outcome at each trial-arm.

sign_dev_o

The sign of the difference between observed and fitted outcome at each trial-arm.

model_assessment

A data-frame on the measures of model assessment: deviance information criterion, number of effective parameters, and total residual deviance.

jagsfit

An object of S3 class jags with the posterior results on all monitored parameters to be used in the mcmc_diagnostics function.

Furthermore, run_ume returns a character vector with the pairwise comparisons observed in the network, obs_comp, and a character vector with comparisons between the non-baseline interventions observed in multi-arm trials only, frail_comp. Both vectors are used in ume_plot function.

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

Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136

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

autojags, jags, prepare_ume, run_model, run_series_meta, ume_plot

Examples

data("nma.liu2013")

# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_liu.rds', package = 'rnmamod'))


# Run random-effects unrelated mean effects model
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_ume(full = res,
        n_chains = 3,
        n_iter = 1000,
        n_burnin = 100,
        n_thin = 1)