multinma (version 0.2.0)

predict.stan_nma: Predictions of absolute effects from NMA models

Description

Obtain predictions of absolute effects from NMA models fitted with nma(). For example, if a model is fitted to binary data with a logit link, predicted outcome probabilities or log odds can be produced.

Usage

# S3 method for stan_nma
predict(
  object,
  ...,
  baseline = NULL,
  newdata = NULL,
  study = NULL,
  trt_ref = NULL,
  type = c("link", "response"),
  level = c("aggregate", "individual"),
  probs = c(0.025, 0.25, 0.5, 0.75, 0.975),
  summary = TRUE
)

Arguments

object

A stan_nma object created by nma().

...

Additional arguments (not used).

baseline

An optional distr() distribution for the baseline response (i.e. intercept) on the linear predictor scale, about which to produce absolute effects. For example, in a model with a logit link, this would be a distribution for the baseline log odds of an event. If NULL, predictions are produced using the baseline response for each study in the network with IPD or arm-based AgD.

newdata

Only required if a regression model is fitted and baseline is specified. A data frame of covariate details, for which to produce predictions. Column names must match variables in the regression model.

If type = "aggregate" this should either be a data frame with integration points as produced by add_integration() (one row per study), or a data frame with individual covariate values (one row per individual) which are summarised over.

If type = "individual" this should be a data frame of individual covariate values, one row per individual.

If NULL, predictions are produced for all studies with IPD and/or arm-based AgD in the network, depending on the value of type.

study

Column of newdata which specifies study names or IDs. When not specified: if newdata contains integration points produced by add_integration(), studies will be labelled sequentially by row; otherwise data will be assumed to come from a single study.

trt_ref

Treatment to which the baseline response distribution refers, if baseline is specified. By default, the baseline response distribution will refer to the network reference treatment. Coerced to character string.

type

Whether to produce predictions on the "link" scale (the default, e.g. log odds) or "response" scale (e.g. probabilities).

level

The level at which predictions are produced, either "aggregate" (the default), or "individual". If baseline is not specified, predictions are produced for all IPD studies in the network if type is "individual" or "aggregate", and for all arm-based AgD studies in the network if type is "aggregate".

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.

See Also

plot.nma_summary() for plotting the predictions.

Examples

# NOT RUN {
## Smoking cessation
# }
# NOT RUN {
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re")
# }
# NOT RUN {
# Predicted log odds of success in each study in the network
predict(smk_fit_RE)

# Predicted probabilities of success in each study in the network
predict(smk_fit_RE, type = "response")

# Predicted probabilities in a population with a baseline log odds of
# response on No Intervention given a Normal distribution with mean -2
# and SD 0.15
(smk_pred_RE <- predict(smk_fit_RE,
                        baseline = distr(qnorm, mean = -2, sd = 0.15),
                        type = "response"))
plot(smk_pred_RE, ref_line = c(0, 1))
# }
# NOT RUN {
## Plaque psoriasis ML-NMR
# }
# NOT RUN {
# Run plaque psoriasis ML-NMR example if not already available
if (!exists("pso_fit")) example("example_pso_mlnmr")
# }
# NOT RUN {
# Predicted probabilities of response in each study in the network
(pso_pred <- predict(pso_fit, type = "response"))
plot(pso_pred, ref_line = c(0, 1))

# Predicted probabilites of response in a new target population, with means
# and SDs or proportions given by
new_agd_int <- data.frame(
  bsa_mean = 0.6,
  bsa_sd = 0.3,
  prevsys = 0.1,
  psa = 0.2,
  weight_mean = 10,
  weight_sd = 1,
  durnpso_mean = 3,
  durnpso_sd = 1
)

# We need to add integration points to this data frame of new data
# We use the weighted mean correlation matrix computed from the IPD studies
new_agd_int <- add_integration(new_agd_int,
                               durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
                               prevsys = distr(qbern, prob = prevsys),
                               bsa = distr(qlogitnorm, mean = bsa_mean, sd = bsa_sd),
                               weight = distr(qgamma, mean = weight_mean, sd = weight_sd),
                               psa = distr(qbern, prob = psa),
                               cor = pso_net$int_cor,
                               n_int = 1000)

# Predicted probabilities of achieving PASI 75 in this target population, given
# a Normal(-1.75, 0.08^2) distribution on the baseline probit-probability of
# response on Placebo (at the reference levels of the covariates), are given by
(pso_pred_new <- predict(pso_fit,
                         type = "response",
                         newdata = new_agd_int,
                         baseline = distr(qnorm, -1.75, 0.08)))
plot(pso_pred_new, ref_line = c(0, 1))
# }