# Estimating the AP and the AUC for Binary Outcome Data.

### Description

This function calculates the estimates of the AP and AUC for binary outcomes as well as their confidence intervals using the perturbation or the nonparametric bootstrap resampling method.

### Usage

1 2 |

### Arguments

`status` |
Binary indicator, 1 indicates case / the class of prediction interest and 0 otherwise. |

`marker` |
Numeric risk score or marker value. Data can be continuous or ordinal. |

`cut.values` |
marker values to use as a cut-off for calculation of positive predictive values (PPV) and true positive fractions (TPF). The default value is NULL. |

`method` |
Method to obtain confidence intervals. The default is method = "none", in which case only point estimates will be given without confidence intervals. If method= "perturbation", then perturbation based CI will be calculated. If method = "bootstrap", then nonparametric bootstrap based CI will be calculated. |

`alpha` |
Confidence level. The default level is 0.95. |

`B` |
Number of resampling to obtain confidence interval. The default value is 1000. |

### Value

an object of class "APBinary" which is a list with components:

`ap_summary` |
Summary of the AP, including the proportion of cases, a point estimate of AP, and their corresponding confidence intervals. |

`auc_summary` |
Summary of the AUC, including a point estimate of AUC with a confidence interval. |

`PPV` |
Available object, positive predictive values at the unique risk score in the data. |

`TPF` |
Available object, true positive fractions at the unique risk score in the data. |

### References

Yuan, Y., Su, W., and Zhu, M. (2015). Threshold-free measures for assessing the performance of medical screening tests. Frontiers in Public Health, 3.57.

Bingying Li (2015) Threshold-free Measure for Assessing the Performance of Risk Prediction with Censored Data, MSc. thesis, Simon Fraser University, Canada

### Examples

1 2 3 4 5 6 7 8 9 | ```
status=c(rep(1,10),rep(0,1),rep(1,18),rep(0,11),rep(1,25),
rep(0,44),rep(1,85),rep(0,176))
marker=c(rep(7,11),rep(6,29),rep(5,69),rep(4,261))
cut.values=sort(unique(marker)[-1])
out1 <- APBinary(status,marker,cut.values)
out1
out2 <- APBinary(status,marker,method="perturbation",
alpha=0.90,B=1500)
out2
``` |