| run_metareg {rnmamod} | R Documentation |
Perform Bayesian pairwise or network meta-regression
Description
Performs a one-stage pairwise or network meta-regression while addressing aggregate binary or continuous missing participant outcome data via the pattern-mixture model.
Usage
run_metareg(
full,
covariate,
covar_assumption,
n_chains,
n_iter,
n_burnin,
n_thin,
inits = NULL
)
Arguments
full |
|
covariate |
A numeric vector or matrix for a trial-specific covariate that is a potential effect modifier. See 'Details'. |
covar_assumption |
Character string indicating the structure of the
intervention-by-covariate interaction, as described in
Cooper et al. (2009). Set |
n_chains |
Positive integer specifying the number of chains for the
MCMC sampling; an argument of the |
n_iter |
Positive integer specifying the number of Markov chains for the
MCMC sampling; an argument of the |
n_burnin |
Positive integer specifying the number of iterations to
discard at the beginning of the MCMC sampling; an argument of the
|
n_thin |
Positive integer specifying the thinning rate for the
MCMC sampling; an argument of the |
inits |
A list with the initial values for the parameters; an argument
of the |
Details
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.
Value
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:
EM |
The estimated summary effect measure (according to the argument
|
beta_all |
The estimated regression coefficient for all possible
pairwise comparisons according to the argument |
dev_o |
The deviance contribution of each trial-arm based on the observed outcome. |
hat_par |
The fitted outcome at each trial-arm. |
phi |
The informative missingness parameter. |
For a fixed-effect network meta-analysis, the output additionally includes:
SUCRA |
The surface under the cumulative ranking (SUCRA) curve for each intervention. |
effectiveneness |
The ranking probability of each intervention for every rank. |
For a random-effects pairwise meta-analysis, the output additionally includes the following elements:
EM_pred |
The predicted summary effect measure (according to the
argument |
delta |
The estimated trial-specific effect measure (according to the
argument |
tau |
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:
abs_risk |
The adjusted absolute risks for each intervention. This
appears only when |
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 |
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.
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
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
See Also
Examples
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)
# 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)