It is often of interest to estimate predictive values assuming the test were applied to a population with a different prevalence of the disease. Projected predictive values may be calculated using Bayes theorem and the relation between predictive values and diagnostic likelihood ratios can be used to derived corresponding confidence intervals.
Details
Predictive values, assuming a certain prevalence 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`.
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)
#> pi ppv_est ppv_lcl ppv_ucl npv_est npv_lcl npv_ucl
#> 0.2000000 0.4060975 0.3636661 0.4499788 0.4139116 0.3516837 0.4790149
pv.prev(pi=0.5, acc=a2)
#> pi ppv_est ppv_lcl ppv_ucl npv_est npv_lcl npv_ucl
#> 0.5000000 0.7322704 0.6956794 0.7659421 0.1500623 0.1194193 0.1868994