Performs a one-stage pairwise or network meta-regression while addressing aggregate binary or continuous missing participant outcome data via the pattern-mixture model.
run_metareg(
full,
covariate,
covar_assumption,
n_chains,
n_iter,
n_burnin,
n_thin,
inits = NULL
)A list of R2jags outputs on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) for the following monitored parameters for a fixed-effect pairwise meta-analysis:
The estimated summary effect measure (according to the argument
measure defined in run_model).
The estimated regression coefficient for all possible
pairwise comparisons according to the argument covar_assumption.
The deviance contribution of each trial-arm based on the observed outcome.
The fitted outcome at each trial-arm.
The informative missingness parameter.
For a fixed-effect network meta-analysis, the output additionally includes:
The surface under the cumulative ranking (SUCRA) curve for each intervention.
The ranking probability of each intervention for every rank.
For a random-effects pairwise meta-analysis, the output additionally includes the following elements:
The predicted summary effect measure (according to the
argument measure defined in run_model).
The estimated trial-specific effect measure (according to the
argument measure defined in run_model).
For a multi-arm trial, we estimate T-1 effects, where T
is the number of interventions in the trial.
The between-trial standard deviation.
In network meta-analysis, EM and EM_pred refer to all
possible pairwise comparisons of interventions in the network. Furthermore,
tau is typically assumed to be common for all observed comparisons
in the network.
For a multi-arm trial, we estimate a total T-1 of delta for
comparisons with the baseline intervention of the trial (found in the first
column of the element t), with T being the number of
interventions in the trial.
Furthermore, the output includes the following elements:
The adjusted absolute risks for each intervention. This
appears only when measure = "OR", measure = "RR", or
measure = "RD".
The leverage for the observed outcome at each trial-arm.
The sign of the difference between observed and fitted outcome at each trial-arm.
A data-frame on the measures of model assessment: deviance information criterion, number of effective parameters, and total residual deviance.
An object of S3 class jags with
the posterior results on all monitored parameters to be used in the
mcmc_diagnostics function.
The run_metareg function also returns the arguments data,
measure, model, assumption, covariate,
covar_assumption, n_chains, n_iter, n_burnin,
and n_thin to be inherited by other relevant functions of the
package.
An object of S3 class run_model.
See 'Value' in run_model.
A numeric vector or matrix for a trial-specific covariate that is a potential effect modifier. See 'Details'.
Character string indicating the structure of the
intervention-by-covariate interaction, as described in
Cooper et al. (2009). Set covar_assumption equal to
"exchangeable", "independent", or "common".
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.
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.
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.
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.
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.
Loukia M. Spineli
run_metareg inherits the arguments data,
measure, model, assumption, heter_prior,
mean_misspar, var_misspar, D, ref,
indic, and base_risk from run_model
(now contained in the argument full). 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_metareg (see 'Examples').
The model runs in JAGS and the progress of the simulation appears on
the R console. The output of run_metareg is used as an S3 object by
other functions of the package to be processed further and provide an
end-user-ready output. 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 models described in Spineli et al. (2021), and Spineli (2019) have been extended to incorporate one study-level covariate variable following the assumptions of Cooper et al. (2009) for the structure of the intervention-by-covariate interaction. The covariate can be either a numeric vector or matrix with columns equal to the maximum number of arms in the dataset.
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
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, 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
autojags, jags,
run_model
data("nma.baker2009")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_baker.rds', package = 'rnmamod'))
# Publication year
pub_year <- c(1996, 1998, 1999, 2000, 2000, 2001, rep(2002, 5), 2003, 2003,
rep(2005, 4), 2006, 2006, 2007, 2007)
# \donttest{
# Perform a random-effects network meta-regression (exchangeable structure)
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_metareg(full = res,
covariate = pub_year,
covar_assumption = "exchangeable",
n_chains = 3,
n_iter = 1000,
n_burnin = 100,
n_thin = 1)
# }
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