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https://www.arca.fiocruz.br/handle/icict/46920
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2023-01-01
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- IFF - Artigos de Periódicos [1287]
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PROBABILITY WAVES: ADAPTIVE CLUSTER-BASED CORRECTION BY CONVOLUTION OF P-VALUE SERIES FROM MASS UNIVARIATE ANALYSIS
Multiple comparisons
Mass univariate analysis
False discovery rate
Cluster-based statistics
Convolution
Affilliation
Fundação Oswaldo Cruz. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. COPPE. Rio de Janeiro, RJ, Brasil.
Abstract
Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error in large exploratory data analysis. This text considers patterns in probability vectors resulting from mass univariate analysis to correct p-values, where clusters of significant p-values may indicate true H0 rejection.
New method: We used ERP experimental data from control and ADHD boys to test the method. The Log10 of p-vector was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. We realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify the type-I error rate (comparing 10,000 pairs of mixed samples whit control and ADHD subjects).
Results: The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution.
Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values.The MC simulations presented 2.78 ± 4.83% of difference (20 channels) from corrected p-vector, while difference from raw p-vector was 596 ± 5.00% (p = 0.0003).
Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to correction.
Keywords
Type-II errorMultiple comparisons
Mass univariate analysis
False discovery rate
Cluster-based statistics
Convolution
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