dot-dca_binary: Fit Bayesian Decision Curve Analysis using Stan for list of...

In giulianonetto/bayesdca: Bayesian Decision Curve Analysis

Fit Bayesian Decision Curve Analysis using Stan for list of models or binary tests

Description

Fit Bayesian Decision Curve Analysis using Stan for list of models or binary tests

Usage

.dca_binary(
  n_thr,
  strategies,
  N,
  d,
  tp,
  tn,
  thresholds,
  prior_p1,
  prior_p2,
  prior_Se1,
  prior_Se2,
  prior_Sp1,
  prior_Sp2,
  N_ext = 0,
  d_ext = 0,
  n_draws = 4000
)

Arguments

n_thr	Number of thresholds (int.).
N	Sample size (vector of integers of length n_thr).
d	Diseased: number of diseased persons or events (vector of integers of length n_thr).
tp	True Positives: number of diseased persons correctly identified as such by the diagnostic test of prediction model (matrix of integers of size n_thr by n_strategies).
tn	True Negatives: number of diseased persons correctly identified as such by the diagnostic test of prediction model (matrix of integers of size n_thr by n_strategies).
thresholds	Numeric vector with probability thresholds with which the net benefit should be computed (default is seq(0.01, 0.5, 0.01)).
N_ext, d_ext	External sample size and number of diseased individuals (or cases), respectively, used to adjust prevalence.
n_strategies	Number of models or binary tests (int.).
prior_p, prior_se, prior_sp	Prior parameters for prevalence, sensitivity, and specificity (numeric matrices of size n_thr by n_strategies).
refresh	Control verbosity of rstan::sampling.
...	Arguments passed to rstan::sampling (e.g. iter, chains).

Value

An object of class stanfit returned by rstan::sampling (e.g. iter, chains)

giulianonetto/bayesdca documentation built on Aug. 31, 2023, 11:07 a.m.