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False positive paradox

About: False positive paradox is a research topic. Over the lifetime, 3497 publications have been published within this topic receiving 117570 citations.


Papers
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Journal ArticleDOI
TL;DR: It is found that Bonferroni-related tests offer little improvement over Bonferronsi, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom.
Abstract: Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is ...

1,146 citations

Journal ArticleDOI
TL;DR: A discussion of the validity assumptions for both overall and sub-effect tests and a multivariate approach which allows exact analysis of such designs are offered and a modification of the univariate approach is also described.
Abstract: Violation of the validity assumptions of repeated measures analysis of variance continues to be a problem in psychophysiology. Such violation results in positive bias for those tests involving the repeated measures factor(s), Recently it has been shown that the tests of simple interactions and multiple comparisons are even more vulnerable to bias (Boik. 1981; Mitzel & Games, 1981). The present paper offers a discussion of the validity assumptions for both overall and sub-effect tests and describes a multivariate approach which allows exact analysis of such designs. A modification of the univariate approach is also described. Validity concerns for both approaches are much less problematic than those of the traditional approach.

1,073 citations

Journal ArticleDOI
TL;DR: A substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs, and a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously is developed.
Abstract: Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.

1,035 citations

Journal ArticleDOI
TL;DR: It was showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.
Abstract: We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.

1,030 citations

Journal ArticleDOI
TL;DR: Although still unfamiliar to many health researchers, the use of false discovery rate control in the context of multiple testing can provide a solid basis for drawing conclusions about statistical significance.

947 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023612
20221,337
2021284
2020272
2019235