S-Space College of Medicine/School of Medicine (의과대학/대학원) Nuclear Medicine (핵의학전공) Journal Papers (저널논문_핵의학전공)
Optimal likelihood-ratio multiple testing with application to Alzheimers disease and questionable dementia
|dc.contributor.author||Kim, Yu Kyeong||-|
|dc.contributor.author||Lee, Dong Soo||-|
|dc.identifier.citation||BMC Medical Research Methodology, 15(1):9||ko_KR|
|dc.description||This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited.
Controlling the false discovery rate is important when testing multiple hypotheses. To enhance the detection capability of a false discovery rate control test, we applied the likelihood ratio-based multiple testing method in neuroimage data and compared the performance with the existing methods.
We analysed the performance of the likelihood ratio-based false discovery rate method using simulation data generated under independent assumption, and positron emission tomography data of Alzheimers disease and questionable dementia. We investigated how well the method detects extensive hypometabolic regions and compared the results to those of the conventional Benjamini Hochberg-false discovery rate method.
Our findings show that the likelihood ratio-based false discovery rate method can control the false discovery rate, giving the smallest false non-discovery rate (for a one-sided test) or the smallest expected number of false assignments (for a two-sided test). Even though we assumed independence among voxels, the likelihood ratio-based false discovery rate method detected more extensive hypometabolic regions in 22 patients with Alzheimers disease, as compared to the 44 normal controls, than did the Benjamini Hochberg-false discovery rate method. The contingency and distribution patterns were consistent with those of previous studies. In 24 questionable dementia patients, the proposed likelihood ratio-based false discovery rate method was able to detect hypometabolism in the medial temporal region.
This study showed that the proposed likelihood ratio-based false discovery rate method efficiently identifies extensive hypometabolic regions owing to its increased detection capability and ability to control the false discovery rate.
|dc.title||Optimal likelihood-ratio multiple testing with application to Alzheimers disease and questionable dementia||ko_KR|
|dc.rights.holder||Lee et al.; licensee BioMed Central.||-|
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