# Sensitivity: Compute sensitivity, specificity and predictive values In ModelGood: Validation of risk prediction models

## Description

Compute sensitivity, specificity and predictive values

## Usage

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

## Arguments

 `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 `binom.test`

## Details

Confidence intervals are obtained with `binom.test`

## Value

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

## Author(s)

Thomas A. Gerds <[email protected]>

binom.test

## Examples

 ```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) ```

### Example output

```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 31, 2017, 2:52 a.m.