BackgroundControlling 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.MethodsWe 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 Alzheimer’s 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.ResultsOur 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 Alzheimer’s 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.ConclusionsThis 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.
Optimal likelihood-ratio multiple testing with application to Alzheimer’s disease and questionable dementia
Donghwan Lee,Hyejin Kang,Eunkyung Kim,Hyekyoung Lee,Heejung Kim,Yu Kyeong Kim,Youngjo Lee,Dong Soo Lee
Published 2015 in BMC Medical Research Methodology
ABSTRACT
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- Publication year
2015
- Venue
BMC Medical Research Methodology
- Publication date
2015-01-30
- Fields of study
Medicine, Psychology
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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