Binomial Distribution Sample Confidence Interval Estimation for Positive and Negative Likelihood Ratio Medical Key Parameters

Likelihood Ratio medical key parameters calculated on categorical results from diagnostic tests are usually express accompanied with their confidence intervals, computed using the normal distribution approximation of binomial distribution. The approximation creates known anomalies, especially for limit cases. In order to improve the quality of estimation, four new methods (called here RPAC, RPAC0, RPAC1, and RPAC2) were developed and compared with the classical method (called here RPWald), using an exact probability calculation algorithm. Computer implementations of the methods use the PHP language. We defined and implemented the functions of the four new methods and the five criterions of confidence interval assessment. The experiments run for samples sizes which vary in 14 – 34 range, 90 – 100 range (0 < X < m, 0 < Y < n), as well as for random numbers for samples sizes (4 ≤ m, n ≤ 1000) and binomial variables (1 ≤ X, Y < m, n). The experiment run shows that the new proposed RPAC2 method obtains the best overall performance of computing confidence interval for positive and negative likelihood ratios