ume.network.data: Make a network object for the unrelated mean effects model...

In bnma: Bayesian Network Meta-Analysis using 'JAGS'

Make a network object for the unrelated mean effects model (inconsistency model) containing data, priors, and a JAGS model file

Description

This is similar to the function network.data, except this is used for the unrelated mean effects model.

Usage

ume.network.data(
  Outcomes,
  Study,
  Treat,
  N = NULL,
  SE = NULL,
  response = NULL,
  Treat.order = NULL,
  type = "random",
  mean.mu = NULL,
  prec.mu = NULL,
  mean.d = NULL,
  prec.d = NULL,
  hy.prior = list("dunif", 0, 5),
  dic = TRUE
)

Arguments

Outcomes	Arm-level outcomes. If it is a multinomial response, the matrix would be arms (row) by multinomial categories (column). If it is binomial or normal, it would be a vector.
Study	A vector of study indicator for each arm
Treat	A vector of treatment indicator for each arm. Treatments should have positive integer values starting from 1 to total number of treatments. In a study, lowest number is taken as the baseline treatment.
N	A vector of total number of observations in each arm. Used for binomial and multinomial responses.
SE	A vector of standard error for each arm. Used only for normal response.
response	Specification of the outcomes type. Must specify one of the following: "normal", "binomial", or "multinomial".
Treat.order	Treatment order which determines how treatments are compared. The first treatment that is specified is considered to be the baseline treatment. Default order is alphabetical. If the treatments are coded 1, 2, etc, then the treatment with a value of 1 would be assigned as a baseline treatment.
type	Type of model fitted: either "random" for random effects model or "fixed" for fixed effects model. Default is "random".
mean.mu	Prior mean for the study effect (baseline risk)
prec.mu	Prior precision for the study effect (baseline risk)
mean.d	Prior mean for the relative effect
prec.d	Prior precision for the relative effect
hy.prior	Prior for the heterogeneity parameter. Supports uniform, gamma, and half normal for normal. It should be a list of length 3, where first element should be the distribution (one of dunif, dgamma, dhnorm, dwish) and the next two are the parameters associated with the distribution. For example, list("dunif", 0, 5) give uniform prior with lower bound 0 and upper bound 5 for the heterogeneity parameter.
dic	This is an indicator for whether user wants to calculate DIC. Model stores less information if you set it to FALSE.

Value

Creates list of variables that are used to run the model using ume.network.run

r	Outcomes made into an array that is suitable for use in rjags code. For multinomial, it has 3 dimensions: number of study by max number of arms in studies by number of categories.
t	Treat transformed into a matrix which has dimensions number of study by max number of arms in studies
nstudy	Number of study
na	Number of arms for each study
ntreat	Number of treatment
b.id	Indicator in sequence of all treatments for which treatment is base treatment in Study
code	Rjags model file code that is generated using information provided by the user. To view model file inside R in a nice format, use cat(network$code).

References

S. Dias, N.J. Welton, A.J. Sutton, D.M. Caldwell, G. Lu, and A.E. Ades (2013), Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials, Medical Decision Making 33(5):641-656. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0272989X12455847")}

Examples

network <- with(thrombolytic, {
 ume.network.data(Outcomes, Study, Treat, N = N, response = "binomial")
})
network

bnma documentation built on May 29, 2024, 11:53 a.m.