summary.crossnma {crossnma}

Summary function for crossnma object

Description

This function creates posterior summary statistics for the fitted cross network meta-analysis / meta-regression model

Usage

## S3 method for class 'crossnma'
summary(
  object,
  quantiles = object$model$quantiles,
  backtransf = object$model$backtransf,
  exp = backtransf,
  ...
)

Arguments

object

An object generated by the crossnma.

quantiles

A numeric vector of probabilities to present posterior summaries. The default value is c(0.025, 0.5, 0.975) for the 95% credible interval and the median.

backtransf

A logical value indicating whether to exponentiate the parameters of relative treatment effect and covariate effect.

exp

Deprecated argument (replaced by backtransf).

...

Additional arguments to be passed to summary() function

Value

crossnma.summary returns a matrix containing the following summary statistics (in columns) for each estimated parameter:

Mean the mean of the posterior distribution

SD the standard deviation of the posterior distribution

2.5% (default) the 2.5% quantile of the posterior distribution (the lower bound of the 95% credible interval)

50% (default) the median of the posterior distribution

97.5% (default) the 97.5% quantile of the posterior distribution (the upper bound of the 95% credible interval)

Rhat Gelman-Rubin statistic. The further the value of Rhat from 1, the worse the mixing of chains and so the convergence.

n.eff An estimate of the effective sample size. The smaller the value of n.eff the greater the uncertainty associated with the corresponding parameter.

Author(s)

Tasnim Hamza [e-mail], Guido Schwarzer [e-mail]

See Also

print.summary.crossnma

Examples

## Not run: 
# We conduct a network meta-analysis assuming a random-effects
# model.
# The data comes from randomized-controlled trials and
# non-randomized studies (combined naively)
head(ipddata) # participant-level data
stddata # study-level data

# Create a JAGS model
mod <- crossnma.model(treat, id, relapse, n, design,
  prt.data = ipddata, std.data = stddata,
  reference = "A", trt.effect = "random", method.bias = "naive")

# Fit JAGS model
set.seed(1909)
fit <- crossnma(mod)

# Display the output
summary(fit)

## End(Not run)