Description Usage Arguments Details Value References Examples

View source: R/bcvusMAR-asyVAR.R

`asyVarVUS`

computes the asymptotic variance 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 VUS.

1 2 3 4 5 6 7 8 9 10 11 12 |

`obj_vus` |
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, which is used for FULL or KNN estimators, or option |

`parallel` |
a logical value. If |

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

For the FULL estimator, a bootstrap resampling process or Jackknife approach is used to estimate the asymptotic variance, whereas, a bootstrap resampling process is employed to obtain the asymptotic variance of K nearest neighbor estimator.

For the full imputation, mean score imputation, inverse probability weighting and semiparametric efficient estimators of VUS, the asymptotic variances are computed by using the explicit form. Furthermore, a bootstrap procedure is also available, useful in case of small sample sizes.

`asyVarVUS`

returns a estimated value of the asymptotic variance.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(EOC)
# 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)
vus.fi <- vus("fi", T = EOC$CA125, Dvec = Dvec.na, V = EOC$V, rhoEst = rho.out,
ci = FALSE)
var.fi <- asyVarVUS(vus.fi, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out)
## Not run:
var.bst.spe <- asyVarVUS(vus.spe, T = EOC$CA125, Dvec = Dvec.na, V = EOC$V,
rhoEst = rho.out, piEst = pi.out, BOOT = TRUE,
parallel = TRUE)
## End(Not run)
``` |

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