multinma (version 0.7.2)

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),
  predictive_distribution = FALSE,
  summary = 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.

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)

predictive_distribution

Logical, when a random effects model has been fitted, should the predictive distribution for relative effects in a new study be returned? Default FALSE.

summary

Logical, calculate posterior summaries? Default TRUE.

See Also

plot.nma_summary() for plotting the relative effects.

Examples

## Smoking cessation
# \donttest{
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)
# }
# \donttest{
# 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
# \donttest{
# Run plaque psoriasis ML-NMR example if not already available
if (!exists("pso_fit")) example("example_pso_mlnmr", run.donttest = TRUE)
# }
# \donttest{
# 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)
# }