relative_effects {multinma} | R Documentation |
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
)
Arguments
x |
A |
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 |
study |
Column of |
all_contrasts |
Logical, generate estimates for all contrasts ( |
trt_ref |
Reference treatment to construct relative effects against, if
|
probs |
Numeric vector of quantiles of interest to present in computed
summary, default |
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 |
summary |
Logical, calculate posterior summaries? Default |
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.
See Also
plot.nma_summary()
for plotting the relative effects.
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)