[Studies and recommendations on the design ov ROC analyses in nuclear medicine].
ROC analysis is the method of choice for an objective assessment of diagnostic tests; however, it requires large sample sizes. We investigated the influence of study design and data analysis on sampling requirements. A sample size of 123 for the patient as well as the control group, is required to prove a difference in sensitivity of 75% vs 90% (specificity 90%, statistical power 0.8). Analysis with the binormal bivariate ROC model allows > 35% reduction in sample size. If the patient group is increased the control group can be smaller, and vice versa; here the correlated ROC model also allows substantial decreases in sample size. If both diagnostic tests are performed in the same patient and control group and evaluated with the correlated ROC model, an objective, statistically sound assessment of diagnostic performance is possible with relatively small samples.
Müller
S P
SP
Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Essen, FRG.
Reiners
C
C
D000704:Analysis of Variance; D016022:Case-Control Studies; D003287:Contrast Media; D006801:Humans; D008962:Models, Theoretical; D011877:Radionuclide Imaging; D012107:Research Design; D012680:Sensitivity and Specificity; D017256:Technetium Tc 99m Sestamibi; D013794:Thallium Radioisotopes; D015899:Tomography, Emission-Computed, Single-Photon
D003287:Contrast Media; D013794:Thallium Radioisotopes; D017256:Technetium Tc 99m Sestamibi
Untersuchungen und Empfehlungen zum Design von ROC-Studien in der Nuklearmedizin.