multinma: relative_effects – R documentation

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relative_effects

Relative treatment effects

Description

Generate (population-average) relative treatment effects. If a ML-NMR or meta-regression model was fitted, these are specific to each study population.

Usage

relative_effects(
  x,
  newdata = NULL,
  study = NULL,
  all_contrasts = FALSE,
  trt_ref = NULL,
  probs = c(0.025, 0.25, 0.5, 0.75, 0.975),
  summary = TRUE
)

Arguments

`x`	A `stan_nma` object created by `nma()`
`newdata`	Only used if a regression model is fitted. A data frame of study details, one row per study, giving the covariate values at which to produce relative effects. Column names must match variables in the regression model. If `NULL`, relative effects are produced for all studies in the network.
`study`	Column of `newdata` which specifies study names, otherwise studies will be labelled by row number.
`all_contrasts`	Logical, generate estimates for all contrasts (`TRUE`), or just the "basic" contrasts against the network reference treatment (`FALSE`)? Default `FALSE`.
`trt_ref`	Reference treatment to construct relative effects against, if `all_contrasts = FALSE`. By default, relative effects will be against the network reference treatment. Coerced to character string.
`probs`	Numeric vector of quantiles of interest to present in computed summary, default `c(0.025, 0.25, 0.5, 0.75, 0.975)`
`summary`	Logical, calculate posterior summaries? Default `TRUE`.

Value

A nma_summary object if summary = TRUE, otherwise a list containing a 3D MCMC array of samples and (for regression models) a data frame of study information.

Examples

## Smoking cessation

# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)


# Produce relative effects
smk_releff_RE <- relative_effects(smk_fit_RE)
smk_releff_RE
plot(smk_releff_RE, ref_line = 0)

# Relative effects for all pairwise comparisons
relative_effects(smk_fit_RE, all_contrasts = TRUE)

# Relative effects against a different reference treatment
relative_effects(smk_fit_RE, trt_ref = "Self-help")

# Transforming to odds ratios
# We work with the array of relative effects samples
LOR_array <- as.array(smk_releff_RE)
OR_array <- exp(LOR_array)

# mcmc_array objects can be summarised to produce a nma_summary object
smk_OR_RE <- summary(OR_array)

# This can then be printed or plotted
smk_OR_RE
plot(smk_OR_RE, ref_line = 1)


## Plaque psoriasis ML-NMR

# Run plaque psoriasis ML-NMR example if not already available
if (!exists("pso_fit")) example("example_pso_mlnmr", run.donttest = TRUE)


# Produce population-adjusted relative effects for all study populations in
# the network
pso_releff <- relative_effects(pso_fit)
pso_releff
plot(pso_releff, ref_line = 0)

# Produce population-adjusted relative effects for a different target
# population
new_agd_means <- data.frame(
  bsa = 0.6,
  prevsys = 0.1,
  psa = 0.2,
  weight = 10,
  durnpso = 3)

relative_effects(pso_fit, newdata = new_agd_means)

multinma

Bayesian Network Meta-Analysis of Individual and Aggregate Data

v0.3.0

GPL-3

Authors

David M. Phillippo [aut, cre] (<https://orcid.org/0000-0003-2672-7841>)

Initial release