A new likelihood approach to inference about predictive values of diagnostic tests in paired designs.
Intuitively, one only needs patients with two positive screening test results for positive predictive values comparison, and those with two negative screening test results for contrasting negative predictive values. Nevertheless, current existing methods rely on the multinomial model that includes superfluous parameters unnecessary for specific comparisons. This practice results in complex statistics formulas. We introduce a novel likelihood approach that fits the intuition by including a minimum number of parameters of interest in paired designs. It is demonstrated that our robust score test statistic is identical to a newly proposed weighted generalized score test statistic. Simulations and real data analysis are used for illustration.
Institute of Statistics, National Central University, Jhongli, Taiwan.
Tsung-Shan
TS
Tsou
D056808:Biostatistics; D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000175:diagnosis; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011237:Predictive Value of Tests
Robust likelihood; diagnostic test; negative predictive value; paired design; positive predictive value