PMC:4620161 / 38909-40425 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/4620161","sourcedb":"PMC","sourceid":"4620161","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4620161","text":"Among the six scenarios, all the testing methods showed proper control of FPR except for L2D with one group of subjects. L2D exhibits high power but at the cost of poor FPR control. This is in part due to the reduction of effect estimates to a positive value regardless the signs of the individual components in ESM. It is possible to reduce this problem in ASM when the sign of the principal kernel is assigned to the resulting L2D measure as shown in (7) and (8). Also, L2D achieved the lowest power with two groups of subjects. AUC simply sums over all the components, significantly misrepresenting the effects when the undershoot becomes moderate. This is reflected in the results where reasonable power is achieved when the undershoot is small and lower power is obtained when the undershoot is moderate. With two groups, AUC performed well in power when the two groups had the same HDR shape, but behaved as poorly as L2D when the two groups had different HDR shapes. As expected, AUC is only sensitive to peak amplitude differences, but is insensitive to shape subtleties. Except for L2D and AUC, the other methods tend to converge in power when the sample size is large enough (e.g., 30 or more). With one group, LME outperformed all other candidates. XUV had a balanced performance on power among all the scenarios, constantly surpassing XMV. Lastly, MVT was slightly more powerful than XUV with two groups when their HDRs were of the same shape with a large number of subjects (e.g., 20 or more per group).","tracks":[]}