To process the elements in the argument base_risk of the
run_model function. It also runs the hierarchical baseline
model, separately from the relative effects model as described in
Dias et al. (2018) and Dias et al. (2013b). The output is to be passed to
run_model and run_metareg to obtain the
(unadjusted and adjusted, respectively) absolute risks for each
intervention in the dataset.
Arguments
- base_risk
A scalar, a vector of length three with elements sorted in ascending order, or a matrix with two columns and number of rows equal to the number of relevant trials. In the case of a scalar or vector, the elements should be in the interval (0, 1). For the matrix, the first column refers to the number of events and the second column to the sample size of the trials comprising the dataset for the baseline model. See 'Details' in
run_model. This argument is only relevant for a binary outcome.- n_chains
Positive integer specifying the number of chains for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 2.- n_iter
Positive integer specifying the number of Markov chains for the MCMC sampling; an argument of the
jagsfunction 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
jagsfunction of the R-package R2jags. The default argument is 1000.- n_thin
Positive integer specifying the thinning rate for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 1.
Value
When base_risk is scalar (fixed baseline), the function
returns the user-defined baseline for the selected reference intervention
in the logit scale. When base_risk is a vector (random baseline),
the function returns a vector with the calculated logit of an event for the
selected reference intervention and its precision. Finally, when
base_risk is a matrix (predicted baseline), the function returns the
following elements:
- ref_base
A vector with the posterior mean and precision of the predicted logit of an event for the selected reference intervention. This vector is be passed to
run_modelandrun_metareg.- figure
A forest plot on the trial-specific observed and estimated baseline risk. See 'Details'.
- table_baseline
A table with the posterior and predictive distribution of the summary baseline mean and the posterior distribution of the between-trial standard deviation in baseline. All results are in the logit scale.
Details
If base_risk is a matrix, baseline_model creates the
hierarchical baseline model in the JAGS dialect of the BUGS language.
The output of this function (see 'Value') constitutes the
posterior mean and precision of the predicted logit of an event for the
selected reference intervention and it is plugged in the WinBUGS code for
the relative effects model (Dias et al., 2013a) via the
prepare_model function. Following (Dias et al., 2013a), a
uniform prior distribution is assigned on the between-trial standard
deviation with upper and lower limit equal to 0 and 5, respectively.
When base_risk is a matrix, the function also returns a forest plot
with the estimated trial-specific probability of an event and 95% credible
intervals (the random effects) alongside the corresponding observed
probability of an event for the selected reference intervention. A grey
rectangular illustrates the summary mean and 95% credible interval of the
random effects.
When base_risk is a matrix (predicted baseline), 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.
References
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network Meta-Analysis for Decision Making. Chichester (UK): Wiley; 2018.
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 2013a;33(5):607–17. doi: 10.1177/0272989X12458724
Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Making 2013b;33(5):657–70. doi: 10.1177/0272989X13485155