compute.threshold.AROC.bsp {AROC} | R Documentation |

Estimates AROC-based threshold values using the semiparametric Bayesian normal linear regression model discussed in Inacio de Carvalho and Rodriguez-Alvarez (2018).

compute.threshold.AROC.bsp(object, newdata, FPF = 0.5)

`object` |
An object of class |

`newdata` |
Data frame with the covariate values at which threshold values are required. |

`FPF` |
Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid. |

Estimation of the covariate-adjusted ROC curve (AROC) using the semiparametric Bayesian normal linear regression model discussed in Inacio de Carvalho and Rodriguez-Alvarez (2018) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population

*F_{\bar{D}}(y|\mathbf{X}_{\bar{D}}) = Pr\{Y_{\bar{D}} ≤q y | \mathbf{X}_{\bar{D}}\}.*

This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value *\mathbf{x}* and a FPF = t, the AROC-based threshold value at the *s*-th posterior sample (*s = 1,…,S*) is calculated as follows

*c^{(s)}_{\mathbf{x}} = \hat{F}^{-1(s)}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).*

from which the posterior mean can be computed

*\hat{c}_{\mathbf{x}} = \frac{1}{S}∑_{s = 1}^{S}c^{(s)}_{\mathbf{x}}.*

As a result, the function provides a list with the following components:

`thresholds.est` |
A matrix with the posterior mean of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

`thresholds.ql` |
A matrix with the posterior 2.5% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

`thresholds.qh` |
A matrix with the posterior 97.5% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.

library(AROC) data(psa) # Select the last measurement newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),] # Log-transform the biomarker newpsa$l_marker1 <- log(newpsa$marker1) m1 <- AROC.bsp(formula.healthy = l_marker1 ~ age, group = "status", tag.healthy = 0, data = newpsa, scale = TRUE, p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE, a = 2, b = 0.5, nsim = 5000, nburn = 1500) # Compute the threshold values FPF = c(0.1, 0.3) newdata <- data.frame(age = seq(52, 80, l = 50)) th_bsp <- compute.threshold.AROC.bsp(m1, newdata, FPF) names(th_bsp)

[Package *AROC* version 1.0-3 Index]