caROC_CB {caROC} | R Documentation |

## Get confidence band for covariate-adjusted ROC curve.

### Description

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

### Usage

```
caROC_CB(diseaseData, controlData, userFormula,
mono_resp_method, global_ROC_controlled_by = "sensitivity",
CB_alpha = 0.95, logit_CB = FALSE, nbootstrap = 100,
nbin = 100, verbose = FALSE)
```

### Arguments

`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". |

`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. |

### Value

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". |

### Author(s)

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

### Examples

```
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 confidence band by controlling sensitivity
### using different monotonicity respecting methods
ROC_CB1 <- caROC_CB(diseaseData,controlData,userFormula,
mono_resp_method = "none",
CB_alpha = 0.95,
nbin = 100,verbose = FALSE)
ROC_CB2 <- caROC_CB(diseaseData,controlData,userFormula,
mono_resp_method = "ROC",
CB_alpha = 0.95,
nbin = 100,verbose = FALSE)
```

*caROC*version 0.1.5 Index]