multinma: posterior_ranks – R documentation

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posterior_ranks

Treatment rankings and rank probabilities

Description

Produce posterior treatment rankings and rank probabilities from a fitted NMA model. When a meta-regression is fitted with effect modifier interactions with treatment, these will differ by study population.

Usage

posterior_ranks(
  x,
  newdata = NULL,
  study = NULL,
  lower_better = TRUE,
  probs = c(0.025, 0.25, 0.5, 0.75, 0.975),
  summary = TRUE
)

posterior_rank_probs(
  x,
  newdata = NULL,
  study = NULL,
  lower_better = TRUE,
  cumulative = FALSE
)

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.
`lower_better`	Logical, are lower treatment effects better (`TRUE`; default) or higher better (`FALSE`)? See details.
`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`.
`cumulative`	Logical, return cumulative rank probabilities? Default is `FALSE`, return posterior probabilities of each treatment having a given rank. If `TRUE`, cumulative posterior rank probabilities are returned for each treatment having a given rank or better.

Details

The function posterior_ranks() produces posterior rankings, which have a distribution (e.g. mean/median rank and 95% Credible Interval). The function posterior_rank_probs() produces rank probabilities, which give the posterior probabilities of being ranked first, second, etc. out of all treatments.

The argument lower_better specifies whether lower treatment effects or higher treatment effects are preferred. For example, with a negative binary outcome lower (more negative) log odds ratios are preferred, so lower_better = TRUE. Conversely, for example, if treatments aim to increase the rate of a positive outcome then lower_better = FALSE.

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 posterior ranks
smk_rank_RE <- posterior_ranks(smk_fit_RE, lower_better = FALSE)
smk_rank_RE
plot(smk_rank_RE)

# Produce rank probabilities
smk_rankprob_RE <- posterior_rank_probs(smk_fit_RE, lower_better = FALSE)
smk_rankprob_RE
plot(smk_rankprob_RE)

# Produce cumulative rank probabilities
smk_cumrankprob_RE <- posterior_rank_probs(smk_fit_RE, lower_better = FALSE,
                                           cumulative = TRUE)
smk_cumrankprob_RE
plot(smk_cumrankprob_RE)

# Further customisation is possible with ggplot commands
plot(smk_cumrankprob_RE) +
  ggplot2::facet_null() +
  ggplot2::aes(colour = Treatment)


## 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 rankings for all study populations in
# the network

# Ranks
pso_rank <- posterior_ranks(pso_fit)
pso_rank
plot(pso_rank)

# Rank probabilities
pso_rankprobs <- posterior_rank_probs(pso_fit)
pso_rankprobs
plot(pso_rankprobs)

# Cumulative rank probabilities
pso_cumrankprobs <- posterior_rank_probs(pso_fit, cumulative = TRUE)
pso_cumrankprobs
plot(pso_cumrankprobs)

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

# Ranks
posterior_ranks(pso_fit, newdata = new_agd_means)

# Rank probabilities
posterior_rank_probs(pso_fit, newdata = new_agd_means)

# Cumulative rank probabilities
posterior_rank_probs(pso_fit, newdata = new_agd_means,
                     cumulative = TRUE)

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