sscaROC_CB {caROC} | R Documentation |

Use this function to compute the confidence band for covariate-adjusted ROC curve, with or without monotonicity respecting methods for sub-population.

sscaROC_CB(diseaseData, controlData, userFormula, mono_resp_method = "none", target_covariates, global_ROC_controlled_by = "sensitivity", CB_alpha = 0.95, logit_CB = FALSE, nbootstrap = 100, nbin = 100, verbose = FALSE)

`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". |

`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", "ROC". "none" is not applying any monotonicity respecting method. "ROC" is to apply ROC-based monotonicity respecting approach. Default value is "ROC". |

`target_covariates` |
Covariates of the interested sub-population. It could be a vector, e.g. c(1, 0.5, 0.8), or a matrix, e.g. target_covariates = matrix(c(1, 0.7, 0.9, 1, 0.8, 0.8), 2, 3, byrow = TRUE) |

`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". |

`CB_alpha` |
Percentage of confidence band. Default is 0.95. |

`logit_CB` |
Whether to use logit-transformed (then transform back) confidence band. Default is FALSE. |

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

`nbin` |
Number of bins used for constructing confidence band. Default is 100. |

`verbose` |
Whether to print out messages during bootstrap. Default value is FALSE. |

If global ROC is controlled by sensitivity, a list will be output including the following

`Sensitivity` |
Vector of sensitivities; |

`Specificity_upper` |
Upper confidence band for specificity estimations; |

`Specificity_lower` |
Lower confidence band for specificity estimations; |

`global_ROC_controlled_by` |
"sensitivity". |

If global ROC is controlled by Specificity, a list will be output including the following

`Specificity` |
Vector of specificity; |

`Sensitivity_upper` |
Upper confidence band for sensitivity estimations; |

`Sensitivity_lower` |
Lower confidence band for sensitivity estimations; |

`global_ROC_controlled_by` |
"specificity". |

Ziyi.li <zli16@mdanderson.org>

n1 = n0 = 500 ## generate data Z_D1 <- rbinom(n1, size = 1, prob = 0.3) Z_D2 <- rnorm(n1, 0.8, 1) Z_C1 <- rbinom(n0, size = 1, prob = 0.7) Z_C2 <- rnorm(n0, 0.8, 1) 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_C1 == 0) + Y_C_Z1 * (Z_C1 == 1) + Z_C2 M1 <- Y_D_Z0 * (Z_D1 == 0) + Y_D_Z1 * (Z_D1 == 1) + 1.5 * Z_D2 diseaseData <- data.frame(M = M1, Z1 = Z_D1, Z2 = Z_D2) controlData <- data.frame(M = M0, Z1 = Z_C1, Z2 = Z_C2) userFormula = "M~Z1+Z2" target_covariates = c(1, 0.7, 0.9) # default nbootstrap is 100 # set nboostrap as 10 here to improve example speed myROCband <- sscaROC_CB(diseaseData, controlData, userFormula, mono_resp_method = "none", target_covariates, global_ROC_controlled_by = "sensitivity", CB_alpha = 0.95, logit_CB = FALSE, nbootstrap = 10, nbin = 100, verbose = FALSE)

[Package *caROC* version 0.1.5 Index]