A general framework for multiple testing dependence.
Ontology highlight
ABSTRACT:
SUBMITTER: Leek JT
PROVIDER: S-EPMC2586646 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:It has been shown that graphical models can be used to leverage the dependence in large-scale multiple testing problems with significantly improved performance (Sun & Cai, 2009; Liu et al., 2012). These graphical models are fully parametric and require that we know the parameterization of f1 - the density function of the test statistic under the alternative hypothesis. However in practice, f1 is often heterogeneous, and cannot be estimated with a simple parametric distribution. We propose a novel semiparametric approach for multiple testing under dependence, which estimates f1 adaptively. This semiparametric approach exactly generalizes the local FDR procedure (Efron et al., 2001) and connects with the BH procedure (Benjamini & Hochberg, 1995). A variety of simulations show that our semiparametric approach outperforms classical procedures which assume independence and the parametric approaches which capture dependence.
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Project description:BackgroundWe consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices.ResultsWe study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable.ConclusionWe discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on pi0 or FDR estimation in a dependency context.
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Project description:Background: Peanut introduction guidelines have undergone significant reversal since 2001 from recommending delayed introduction to rescinding the recommendations in 2008 to actively recommending early introduction of peanut between 4 and 11 months of age in high-risk infants in 2015. This qualitative study aims to explore pediatrician beliefs, practices, facilitators, and barriers regarding peanut introduction and testing. Methods: General pediatricians from academic, private, large group, and underserved practices in Northern California underwent individual semi-structured interviews in 2017. We asked about experiences surrounding infant peanut introduction, strategies for staying up-to-date with current recommendations, and barriers and facilitators to the new peanut introduction and testing recommendations. The data were coded, and using grounded theory methodology, a conceptual framework was developed around early peanut introduction and testing in infants. Results: Eighteen general pediatricians participated. We identified barriers that may contribute to pediatrician reluctance to recommending early peanut introduction or testing including lack of awareness, lack of agreement, lack of resources, and lack of outcome expectancy. A framework was created that suggests that pediatricians need to be knowledgeable about new recommendations, agree with the recommendations, have resources to carry out the counseling and testing, and have buy-in from the parents in order for successful uptake of peanut introduction guidelines. Conclusion: Recommending early peanut introduction or testing causes significant apprehension in some pediatricians, and there are many barriers to following recent early peanut introduction recommendations. A potential limitation of the study is that it was conducted right after the addendum guidelines were changed, leaving the possibility that attitudes and practices may have evolved since 2017. It is still likely that a multifaceted approach that addresses primary care provider guideline awareness, limited primary care resources for education and testing, and includes support and collaboration from subspecialty practices is more likely to lead to improved early peanut introduction uptake.
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