Description Usage Arguments Value Author(s) Examples

Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve.

1 2 3 4 5 |

`diseaseData` |
Data from patients including dependent (biomarker) and independent (covariates) variables. |

`controlData` |
Data from controls including dependent (biomarker) and independent (covariates) variables. |

`userFormula` |
A character string to represent the function for covariate adjustment. For example, let Y denote biomarker, Z1 and Z2 denote two covariates. Then userFormula = "Y ~ Z1 + Z2". |

`control_sensitivity` |
The level(s) of sensitivity to be controlled at. Could be a scalar (e.g. 0.7) or a numeric vector (e.g. c(0.7, 0.8, 0.9)). |

`control_specificity` |
The level(s) of specificity to be controlled at. Could be a scalar (e.g. 0.7) or a numeric vector (e.g. c(0.7, 0.8, 0.9)). |

`mono_resp_method` |
The method used to restore monotonicity of the ROC curve or computed sensitivity/specificity value. It should one from the following: "none", "regression", "ROC". "none" is not applying any monotonicity respecting method. "regression" is to apply the approach by Huang (2017) on quantile regression step. "ROC" is to apply ROC-based monotonicity respecting approach. Default value is "ROC". |

`whichSE` |
The method used to compute standard error. It should be one from the following: "sample", "bootstrap", meaning to calculate the standard error using sample-based approach or bootstrap. Default is "sample". |

`global_ROC_controlled_by` |
Whether sensitivity/specificity is used to control when computing global ROC. It should one from the following: "sensitivity", "specificity". Default is "sensitivity". |

`nbootstrap` |
Number of boostrap iterations. Default is 100. |

`CI_alpha` |
Percentage of confidence interval. Default is 0.95. |

`logit_CI` |
Whether to apply logit-based confidence interval. Logit-transformed CI has been identified to be more robust near border area. |

`verbose` |
Whether to print out messages. Default value is true. |

If control_sensitivity or control_specificity is provided, compute covariate-adjusted specificity (sensitivity) at controlled sensitivity (specificity) level.

`Estimate` |
Covariate-adjusted sensitivity/specificity. |

`SE` |
Estimated standard error. |

`CI` |
Estimated confidence intervals. |

If both control_sensitivity and control_specificity are null, compuate covariate-adjusted ROC curve.

`sensitivity` |
Estimated sensitivity. |

`specificity` |
Estimated specificity. |

`mono_adj` |
Monotonicity adjustment method. |

Ziyi.li <ziyi.li@emory.edu>

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 | ```
n1 = n0 = 500
## generate data
Z_D <- rbinom(n1, size = 1, prob = 0.3)
Z_C <- rbinom(n0, size = 1, prob = 0.7)
Y_C_Z0 <- rnorm(n0, 0.1, 1)
Y_D_Z0 <- rnorm(n1, 1.1, 1)
Y_C_Z1 <- rnorm(n0, 0.2, 1)
Y_D_Z1 <- rnorm(n1, 0.9, 1)
M0 <- Y_C_Z0 * (Z_C == 0) + Y_C_Z1 * (Z_C == 1)
M1 <- Y_D_Z0 * (Z_D == 0) + Y_D_Z1 * (Z_D == 1)
diseaseData <- data.frame(M = M1, Z = Z_D)
controlData <- data.frame(M = M0, Z = Z_C)
userFormula = "M~Z"
## calculate covariate-adjusted specificity at
## controlled sensitivity levels (0.2, 0.8, 0.9)
caROC(diseaseData,controlData,userFormula,
control_sensitivity = c(0.2,0.8, 0.9),
control_specificity = NULL,mono_resp_method = "ROC",
whichSE = "bootstrap",nbootstrap = 100,
CI_alpha = 0.95, logit_CI = TRUE)
## calculate covariate-adjusted sensitivity at
## controlled specificity levels (0.2, 0.8, 0.9)
caROC(diseaseData,controlData,userFormula,
control_sensitivity = NULL,
control_specificity = c(0.7,0.8, 0.9),mono_resp_method = "none",
whichSE = "sample",nbootstrap = 100,
CI_alpha = 0.95, logit_CI = TRUE)
## calculate the whole covariate-adjusted ROC curve
ROC1 <- caROC(diseaseData,controlData,userFormula = "M~Z",
mono_resp_method = "none")
ROC2 <- caROC(diseaseData,controlData,userFormula = "M~Z",
mono_resp_method = "regression")
ROC3 <- caROC(diseaseData,controlData,userFormula = "M~Z",
mono_resp_method = "ROC")
``` |

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