pv.prev: Compute predictive values for theoretical prevalences

In DTComPair: Comparison of Binary Diagnostic Tests in a Paired Study Design

Compute predictive values for theoretical prevalences

Description

It is often of interest to estimate predcitive values assuming the test were applied to a population with a different prevalance of the disease. Projected predictive values may be calculated using Bayes theorem and the relation between predictive values and diganostic likelihood ratios can be used to derived corresponding confidence intervals.

Usage

pv.prev(pi, acc)

Arguments

pi	A theoretical prevalance of the disease (proportion).
acc	An object of class 'acc.1test'.

Details

Predictive values, assuming a certain prevalance of the disease, are derived using the relation between predictive values and diagnostic likelihood ratios:

- PPV = 1 / 1 + (1 / pi - 1) / pDLR - NPV = 1 / 1 + (1 / (1 / pi - 1)) / nDLR.

See Newcombe RG (2013). Confidence Intervals for Proportions and Related Measures of Effect Size. Chapman and Hall/ CRC Biostatistics Series (chapters 12.3+5 and 14.9).

The alpha-level of (1-alpha)-confidence intervals is inherited from 'acc.1test'.

Value

A vector containing the projected values.

See Also

[acc.1test()]

Examples

data(Paired1) # Hypothetical study data 
a1 <- tab.1test(d=d, y=y1, data=Paired1)
a2 <- acc.1test(a1, alpha = 0.05)
pv.prev(pi=0.2, acc=a2)
pv.prev(pi=0.5, acc=a2)

DTComPair documentation built on Aug. 20, 2023, 5:06 p.m.