min_possible_prevalence {BayesianReasoning} | R Documentation |

## Show minimum possible prevalence given the test characteristics

### Description

Given a FP and a desired PPV, what is the Minimum Prevalence of a Condition

### Usage

```
min_possible_prevalence(Sensitivity = 95, FP_test = 1, min_PPV_desired = 90)
```

### Arguments

`Sensitivity` |
Sensitivity of the test: [0-100] |

`FP_test` |
False positive rate (1-Specificity): [0-100] |

`min_PPV_desired` |
Which PPV is what you consider the minimum to trust a positive result in the test: [0-100] |

### Value

A description showing the minimum necessary prevalence.

### Examples

```
# Example 1
min_possible_prevalence(Sensitivity = 99.9, FP_test = .1, min_PPV_desired = 70)
"To reach a PPV of 70 when using a test with 99.9 % Sensitivity and 0.1 % False Positive Rate,
you need a prevalence of at least 1 out of 429"
# Example 2
min_possible_prevalence(100, 0.1, 98)
"To reach a PPV of 98 when using a test with 100 % Sensitivity and 0.1 % False Positive Rate,
you need a prevalence of at least 1 out of 21"
```

[Package

*BayesianReasoning*version 0.4.2 Index]