[R-sig-Epi] Estimating CI for sensitivity and specificity

[Mark Stevenson]

The 'epicentre' package (see the link on http://epicentre.massey.ac.nz/) has
a function (epi.tests) that returns point estimates and confidence intervals
on true and apparent prevalence, sensitivity, specificity, positive and
negative predictive values, and positive and negative likelihood ratios from
count data provided in a 2 by 2 table.

The following is the example from the function documentation:

## A new diagnostic test was trialled on 2000 patients. Of 1000 patients
## that were disease positive, 750 tested positive. Of 1000 patients that 
## were disease negative, 550 tested negative. What is the likeliood
## ratio of a positive test?

epi.tests(750, 450, 250, 550, alpha = 0.05, verbose = FALSE)

          Disease +    Disease -      Total
Test +          750          450       1200
Test -          250          550        800
Total          1000         1000       2000

Point estimates and 95 % CIs:
---------------------------------------------------------
Apparent prevalence:                    0.6 (0.58, 0.62)
True prevalence:                        0.5 (0.48, 0.52)
Sensitivity:                            0.75 (0.72, 0.78)
Specificity:                            0.55 (0.52, 0.58)
Positive predictive value:              0.62 (0.6, 0.65)
Negative predictive value:              0.69 (0.65, 0.72)
Likelihood ratio positive:              1.67 (1.54, 1.8)
Likelihood ratio negative:              0.45 (0.4, 0.51)
---------------------------------------------------------

## The likelihood ratio of a positive test was 1.67 (95% CI 1.54 to 1.8).

Hope this is useful. 

Regards,

Mark Stevenson

************************************************* 
Mark Stevenson 
Associate Professor, Veterinary Epidemiology 
IVABS, Massey University 
Private Bag 11-222 
Palmerston North New Zealand 
Ph: + 64 (06) 350 5915 
Fx: + 64 (06) 350 5716 
M.Stevenson at massey.ac.nz