The control of the false discovery rate in fixed sequence multiple testing

Lynch, Gavin; Sarkar, Santanu; Finner, Helmut

doi:10.1214/17-ejs1359

Cited by 20 publications

(14 citation statements)

References 38 publications

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“…In all statistical analyses, age, gender, education, disease duration, smoking status, and BMI were used as covariates. False discovery rate (FDR) [ 35 ] was used to correct multiple testing in the present study.…”

Section: Methodsmentioning

confidence: 99%

Gut microbial biomarkers for the treatment response in first-episode, drug-naïve schizophrenia: a 24-week follow-up study

Yuan

Wang

et al. 2021

Transl Psychiatry

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Preclinical studies have shown that the gut microbiota can play a role in schizophrenia (SCH) pathogenesis via the gut-brain axis. However, its role in the antipsychotic treatment response is unclear. Here, we present a 24-week follow-up study to identify gut microbial biomarkers for SCH diagnosis and treatment response, using a sample of 107 first-episode, drug-naïve SCH patients, and 107 healthy controls (HCs). We collected biological samples at baseline (all participants) and follow-up time points after risperidone treatment (SCH patients). Treatment response was assessed using the Positive and Negative Symptoms Scale total (PANSS-T) score. False discovery rate was used to correct for multiple testing. We found that SCH patients showed lower α-diversity (the Shannon and Simpson’s indices) compared to HCs at baseline (p = 1.21 × 10−9, 1.23 × 10−8, respectively). We also found a significant difference in β-diversity between SCH patients and HCs (p = 0.001). At baseline, using microbes that showed different abundance between patients and controls as predictors, a prediction model can distinguish patients from HCs with an area under the curve (AUC) of 0.867. In SCH patients, after 24 weeks of risperidone treatment, we observed an increase of α-diversity toward the basal level of HCs. At the genus level, we observed decreased abundance of Lachnoclostridium (p = 0.019) and increased abundance Romboutsia (p = 0.067). Moreover, the treatment response in SCH patients was significantly associated with the basal levels of Lachnoclostridium and Romboutsia (p = 0.005 and 0.006, respectively). Our results suggest that SCH patients may present characteristic microbiota, and certain microbiota biomarkers may predict treatment response in this patient population.

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Section: Methodsmentioning

confidence: 99%

Gut microbial biomarkers for the treatment response in first-episode, drug-naïve schizophrenia: a 24-week follow-up study

Yuan

Wang

et al. 2021

Transl Psychiatry

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“…Though aiming for addressing a similar issue, our method is motivated from the empirical Bayes perspective, and it is built on the two-group mixture model that allows the prior probabilities of being null to vary across different hypotheses. The implementation and theoretical analysis of our method are also quite different from those in [32,36].…”

Section: Introductionmentioning

confidence: 95%

“…To account for the potential mistakes in the ranked list or to improve power by incorporating external covariates, alternative methods have been proposed in the literature. For example, [36] extends the fixed sequence method to allow more than one acceptance before stopping. [32] modifies AdaPT in [30] by giving analysts the power to enforce the ordered constraint on the final rejection set.…”

Section: Introductionmentioning

confidence: 99%

Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information

Cao¹,

Zhang²

2021

Preprint

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Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of p-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.

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“…Ordered hypothesis testing considers tests for which additional prior information is available, and allows sorting null hypotheses from least to most promising [19,18,20,14]. In these papers, however, the word "sequential" or "ordered" does not refer to online testing; these methods are set in an offline environment, requiring access to all p-values at once.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Online Testing of Multiple Hypotheses

Zrnic,

Ramdas,

Jordan

2018

Preprint

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We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times. This setting increasingly characterizes real-world applications in science and industry, where teams of researchers across large organizations may conduct tests of hypotheses in a decentralized manner. The overlap in time and space also tends to induce dependencies among test statistics, a challenge for classical methodology, which either assumes (overly optimistically) independence or (overly pessimistically) arbitrary dependence between test statistics. We present a general framework that addresses both of these issues via a unified computational abstraction that we refer to as "conflict sets." We show how this framework yields algorithms with formal FDR guarantees under a more intermediate, local notion of dependence. We illustrate these algorithms in simulation experiments, comparing to existing algorithms for online FDR control.

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The control of the false discovery rate in fixed sequence multiple testing

Cited by 20 publications

References 38 publications

Gut microbial biomarkers for the treatment response in first-episode, drug-naïve schizophrenia: a 24-week follow-up study

Gut microbial biomarkers for the treatment response in first-episode, drug-naïve schizophrenia: a 24-week follow-up study

Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information

Asynchronous Online Testing of Multiple Hypotheses

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