Evidence map›Paper›PMID 39222026›Full record

ArticleBiometrics2024

Summary statistics knockoffs inference with family-wise error rate control.

Catherine Xinrui Yu, Jiaqi Gu, Zhaomeng Chen, Zihuai He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Catherine Xinrui YuDepartment of Statistics, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Jiaqi GuDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, California, 94304, United States.ORCID 0000-0002-8773-9558
Zhaomeng ChenDepartment of Statistics, Stanford University, Stanford, California, 94305, United States.
Zihuai HeDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, California, 94304, United States.ORCID 0000-0002-8220-4183

Funding

Stanford Alzheimer's Disease Research CenterAdmin Supp: Developing iPSC models for AD and PDP30AG066515 · NIA · STANFORD UNIVERSITY · PI Lisa Goldman Rosas · 2020 to 2026
$29.0M
Statistical and computational methods for integrative analysis of Alzheimer's Disease geneticsR01AG066206 · NIA · STANFORD UNIVERSITY · PI HE, ZIHUAI · 2019 to 2023
$3.5M
NIA NIH HHS P30 AG066515NIA NIH HHS R01 AG066206NIH HHS AG066206
6 · The paper itself

Abstract

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

Indexed as

AlgorithmsAlzheimer DiseaseComputer SimulationBiometryData Interpretation, StatisticalHumansModels, StatisticalAlzheimer’s disease geneticsconditional independencefamily-wise error rateknockoffssummary statistics

Identifiers

PMID39222026
PMCPMC11367731

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.