Description Usage Arguments Details Value Author(s) See Also Examples

Compute sensitivity, specificity and predictive values

1 2 3 4 | ```
Sensitivity(x,event,cutoff,comparison=">=",...)
Specificity(x,event,cutoff,comparison=">=",...)
NPV(x,event,cutoff,comparison=">=",...)
PPV(x,event,cutoff,comparison=">=",...)
``` |

`x` |
Either a binary 0,1 variable, or a numeric marker which is cut into binary. |

`event` |
Binary response variable. Either a 0,1 variable where 1 means 'event', or a factor where the second level means 'event'. |

`cutoff` |
When x is a numeric marker, it is compared to this cutoff to obtain a binary test. |

`comparison` |
How x is to be compared to the cutoff value |

`...` |
passed on to |

Confidence intervals are obtained with `binom.test`

list with Sensitivity, Specificity, NPV, PPV and confidence interval

Thomas A. Gerds <[email protected]>

binom.test

1 2 3 4 5 6 7 8 9 | ```
set.seed(17)
x <- rnorm(10)
y <- rbinom(10,1,0.4)
Sensitivity(x,y,0.3)
Specificity(x,y,0.3)
PPV(x,y,0.3)
NPV(x,y,0.3)
Diagnose(x,y,0.3)
``` |

```
Sensitivity: 16.7 (CI_95:[0.4,64.1])
Specificity: 25 (CI_95:[0.6,80.6])
Positive predictive value: 25 (CI_95:[0.6,80.6])
Negative predictive value: 16.7 (CI_95:[0.4,64.1])
2x2 table:
event
test 0 1
0 1 5
1 3 1
Diagnostic parameters:
Parameter Estimate CI.95
[1,] Sensitivity 16.7 [0.4,64.1]
[2,] Specificity 25 [0.6,80.6]
[3,] PPV 25 [0.6,80.6]
[4,] NPV 16.7 [0.4,64.1]
```

ModelGood documentation built on May 29, 2017, 4:14 p.m.

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