true_AUC_var_abitrary_Rcpp {NestMRMC}

Calculate the between-cases AUC estimator's theoretical variance and covariance

Description

This function calculates between-cases AUC estimator's theoretical variance and covariance based on all the truths, namely, the ROI's truth labels, AUC values, covariance between ROI scores within same reader, scale factor that influences the covariance between ROI scores between readers and the variances for positive and negative ROI scores. Detailed formulas are available in following paper: Single Reader Between-Cases AUC Estimator with Nested Data. Statistical Methods in Medical Research. https://doi.org/10.1177/09622802221111539. There is also a none Rcpp version of this function in this package. The function name is 'true_AUC_var_abitrary', which is slower but no need to install Rcpp. They produce the exact same results.

Usage

true_AUC_var_abitrary_Rcpp(
  numROI,
  AUC = 0.7,
  cov = 0.5,
  rho = 0.5,
  sigma_pos = 1,
  sigma_neg = 1
)

Arguments

numROI

The number of positive and negative ROIs in all the patients.

AUC

The AUC values used in simulated data.

cov

The covariance used in simulating reading scores.

rho

The scale factor used in simulating reading scores.

sigma_pos

The variacne for positive ROI's reading score, defalut is 1.

sigma_neg

The variacne for negative ROI's reading score, defalut is 1.

Value

The theoretical AUC estimator's (co)variance based on the simulation settings.