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

## Smoking cessation

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


# 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 67 observed events out of 566
# individuals on No Intervention, corresponding to a Beta(67, 566 - 67)
# distribution on the baseline probability of response, using
# `baseline_type = "response"`
(smk_pred_RE <- predict(smk_fit_RE,
                        baseline = distr(qbeta, 67, 566 - 67),
                        baseline_type = "response",
                        type = "response"))
plot(smk_pred_RE, ref_line = c(0, 1))

# Predicted probabilities in a population with a baseline log odds of
# response on No Intervention given a Normal distribution with mean -2
# and SD 0.13, using `baseline_type = "link"` (the default)
# Note: this is approximately equivalent to the above Beta distribution on
# the baseline probability
(smk_pred_RE2 <- predict(smk_fit_RE,
                         baseline = distr(qnorm, mean = -2, sd = 0.13),
                         type = "response"))
plot(smk_pred_RE2, ref_line = c(0, 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)


# 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))