Description Usage Arguments Details Value References Examples

`asyCovTCF`

computes the asymptotic variance-covariance matrix of full data (FULL) and bias-corrected estimators (i.e. full imputation, mean score imputation, inverse probability weighting, semiparametric efficient and K nearest neighbor) of TCFs.

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`obj_tcf` |
a result of a call to |

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

`Dvec` |
a n * 3 binary matrix with three columns, corresponding to the 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 |

`BOOT` |
a logical value. Default = |

`nR` |
the number of bootstrap replicates, used when |

`parallel` |
a logical value. If |

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

For bias-corrected estimators of TCFs, the asymptotic variance-covariance matrix at a fixed cut point is estimated by using the Delta method. The function `asyCovTCF`

implements the explicit forms presented in To Duc et al. (2016a, 2016b). In addition, the bootstrap procedure is also available.

For FULL estimator, the asymptotic variance-covariance matrix is computed via bootstrap only.

This function returns an estimated asymptotic variance-covariance matrix for FULL estimator and bias-corrected estimators of TCFs at a fixed cut point.

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

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
data(EOC)
# FULL data estimator
Dfull <- preDATA(EOC$D.full, EOC$CA125)
Dvec.full <- Dfull$Dvec
full.tcf <- ROCs.tcf("full", T = EOC$CA125, Dvec = Dvec.full, cps = c(2, 4))
full.var <- asyCovTCF(full.tcf, T = EOC$CA125, Dvec = Dvec.full)
# Preparing the missing disease status
Dna <- preDATA(EOC$D, EOC$CA125)
Dfact.na <- Dna$D
Dvec.na <- Dna$Dvec
rho.out <- rhoMLogit(Dfact.na ~ CA125 + CA153 + Age, data = EOC, test = TRUE)
## FI estimator
fi.tcf <- ROCs.tcf("fi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out, cps = c(2,4))
fi.var <- asyCovTCF(fi.tcf, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out)
## MSI estimator
msi.tcf <- ROCs.tcf("msi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out, cps = c(2,4))
msi.var <- asyCovTCF(msi.tcf, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out)
## IPW estimator
pi.out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE)
ipw.tcf <- ROCs.tcf("ipw", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
piEst = pi.out, cps = c(2,4))
ipw.var <- asyCovTCF(ipw.tcf, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
piEst = pi.out)
## SPE estimator
spe.tcf <- ROCs.tcf("spe", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out, piEst = pi.out, cps = c(2,4))
spe.var <- asyCovTCF(spe.tcf, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out, piEst = pi.out)
## KNN estimators
XX <- cbind(EOC$CA125, EOC$CA153, EOC$Age)
rho.1nn <- rhoKNN(X = XX, Dvec = Dvec.na, V = EOC$V, K = 1, type = "mahala")
knn.tcf <- ROCs.tcf("knn", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.1nn, cps = c(2,4))
knn.var <- asyCovTCF(knn.tcf, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.1nn)
``` |

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