vus {bcROCsurface} | R Documentation |

`vus`

computes bias-corrected estimates of the volume under the ROC surface for evaluating the accuracy of a continuous diagnostic test.

vus( method = "full", T, Dvec, V, rhoEst = NULL, piEst = NULL, ci = TRUE, ci.level = ifelse(ci, 0.95, NULL), BOOT = FALSE, nR = ifelse(ci, 250, NULL), parallel = FALSE, ncpus = ifelse(parallel, detectCores()/2, NULL), trace = TRUE )

`method` |
name of bias-corrected estimation method to be used for estimating the VUS in presence of verification bias. See |

`T` |
a numeric vector containing the diagnostic test values. |

`Dvec` |
a n * 3 binary matrix with the three columns, corresponding to three classes of the disease status. In row i, 1 in column j indicates that the i-th subject belongs to class j, with j = 1, 2, 3. A row of |

`V` |
a binary vector containing the verification status (1 verified, 0 not verified). |

`rhoEst` |
a result of a call to |

`piEst` |
a result of a call to |

`ci` |
a logical value. If TRUE (default), computes an confidence interval of VUS and tests the null hypothesis H0: VUS = 1/6. |

`ci.level` |
an confidence level to be used for constructing the confidence interval; default 0.95. |

`BOOT` |
a logical value. Default = |

`nR` |
the number of bootstrap replicates, which is used for FULL or KNN estimator, or option |

`parallel` |
a logical value. If |

`ncpus` |
number of processes to be used in parallel computing. Default is a half of available cores. |

`trace` |
a logical value. If |

The function implements five bias-corrected estimation methods in To Duc et al. (2016, 2018) for estimating VUS of a three-class continuous diagnostic test in presence of verification bias. The estimators are full imputation (FI), mean score imputation (MSI), inverse probability weighted (IPW), semiparametric efficient (SPE) and K nearest-neighbor (KNN), see `ROCs`

. These esitmators work under MAR assumption.

The standard error of the estimates are obtained through the function `asyVarVUS`

. In particular, the standard error of the FULL estimate is computed by bootrap resampling method or by Jackknife approach proposed in Guangming et al. (2013). For the bias-corrected estimates, the standard errors are computed by using asymptotic theory (with respect to FI, MSI, IPW and SPE estimator) or bootstrap resampling method (with respect to KNN estimator). A confidence interval of VUS also is given. A logit transformation is also applied for obtaining the confidence interval.

The default value of the number of bootstrap replicates is 250.

Note that, before apply the functions `vus`

, the use of `preDATA`

might be needed to check the monotone ordering disease classes and to create the matrix format for disease status.

`vus`

returns an object of class inheriting from "vus" class.

The function `print.vus`

can be used to print a summary of the results.

An object of class "vus" is a list containing at least the following components:

`vus.fit` |
the estimate of VUS. |

`std` |
the standard error, obtained by using asymptotic theory or bootstrap resampling method. |

`call` |
the matched call. |

`t.stat` |
t-statistic. |

`p.val_norm` |
p-value correspond to normal-test. |

`ci.norm` |
the confidence interval of VUS by using normal approximation. |

`ci.logit` |
the confidence interval of VUS via logit transform. |

`ci.level` |
the confidence level used. |

`BOOT` |
the value of |

`nR` |
the number of bootstrap replicates used. |

In addition, the name of method used to estimate VUS also is given as the attribute of `vus.fit`

.

To Duc, K., Chiogna, M. and Adimari, G. (2018)
Nonparametric estimation of ROC surfaces in presence of verification bias.
*REVSTAT Statistical Journal*. Accepted.

To Duc, K., Chiogna, M. and Adimari, G. (2016)
Bias-corrected methods for estimating the receiver operating characteristic surface of continuous diagnostic tests.
*Electronic Journal of Statistics*, **10**, 3063-3113.

Guangming, P., Xiping, W. and Wang, Z. (2013)
Non-parameteric statistical inference for $P(X < Y < Z)$.
*Sankhya A*, **75**, 1, 118-138.

data(EOC) head(EOC) ## Not run: # FULL data estimator Dfull <- preDATA(EOC$D.full, EOC$CA125) Dvec.full <- Dfull$Dvec vus("full", T = EOC$CA125, Dvec = Dvec.full) ## End(Not run) # Preparing the missing disease status Dna <- preDATA(EOC$D, EOC$CA125) Dfact.na <- Dna$D Dvec.na <- Dna$Dvec # FI estimator rho.out <- rhoMLogit(Dfact.na ~ CA125 + CA153 + Age, data = EOC, test = TRUE) vus("fi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out) ## Not run: # MSI estimator vus("msi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out) # IPW estimator pi.out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE) vus("ipw", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, piEst = pi.out) # SPE estimator vus("spe", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out, piEst = pi.out) # KNN estimator, K = 1, Mahalanobis distance XX <- cbind(EOC$CA125, EOC$CA153, EOC$Age) rho.maha.1nn <- rhoKNN(X = XX, Dvec = Dvec.na, V = EOC$V, K = 1, type = "mahala") vus("knn", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.maha.1nn) ## End(Not run)

[Package *bcROCsurface* version 1.0-4 Index]