Subtopic Deep Dive
False Discovery Rate Control
Research Guide
What is False Discovery Rate Control?
False Discovery Rate (FDR) control develops procedures like Benjamini-Hochberg to manage the expected proportion of false positives among significant results in multiple hypothesis testing within clinical trials.
FDR procedures address high-dimensional data testing in genomics and imaging from trials, balancing type I and II errors under independence or dependence. Benjamini and Yekutieli (2001) extended the Benjamini-Hochberg method to dependent tests, with 10532 citations. Wacholder et al. (2004) applied FDR to assess false positives in molecular epidemiology, cited 1770 times.
Why It Matters
FDR control enables reliable biomarker discovery in clinical trials with massive tests from genomics and imaging, reducing false positives that mislead drug development. Benjamini and Yekutieli (2001) showed dependency-adapted FDR maintains power in correlated trial data. Wacholder et al. (2004) quantified false discovery risks in genetic associations, guiding trial designs to validate candidates. Nuzzo (2014) highlighted p-value misinterpretation leading to irreproducible findings in trials.
Key Research Challenges
Dependency in Test Statistics
Correlated test statistics in genomics and imaging violate independence assumptions of basic Benjamini-Hochberg. Benjamini and Yekutieli (2001) derived conservative adjustments but at power cost. Developing powerful dependence-robust FDR remains critical for trial data.
Power vs Conservatism Tradeoff
FDR methods like Benjamini-Hochberg sacrifice power for error control in high dimensions. Colquhoun (2014) showed p=0.05 yields 30%+ false discoveries in underpowered trials. Adaptive procedures balancing power and FDR in sparse signals challenge clinical applications.
Evaluation in Realistic Scenarios
Simulation studies struggle to mimic trial complexities like missing data and heterogeneity. Morris et al. (2019) emphasized simulations reveal method behaviors under true parameters. Validating FDR across diverse trial settings requires advanced evaluation frameworks.
Essential Papers
The control of the false discovery rate in multiple testing under dependency
Yoav Benjamini, Daniel Yekutieli · 2001 · The Annals of Statistics · 10.5K citations
Benjamini and Hochberg suggest that the false discovery rate may\nbe the appropriate error rate to control in many applied multiple testing\nproblems. A simple procedure was given there as an FDR c...
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies
Sholom Wacholder, Stephen J. Chanock, Montserrat García‐Closas et al. · 2004 · JNCI Journal of the National Cancer Institute · 1.8K citations
Abstract Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strat...
Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration
Veronika Skrivankova, Rebecca C. Richmond, Benjamin Woolf et al. · 2021 · BMJ · 1.6K citations
Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often hampered by inadequate report...
Scientific method: Statistical errors
Regina Nuzzo · 2014 · Nature · 1.5K citations
Using simulation studies to evaluate statistical methods
Tim P. Morris, Ian R. White, Michael J. Crowther · 2019 · Statistics in Medicine · 1.1K citations
Simulation studies are computer experiments that involve creating data by pseudo‐random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical metho...
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
Mariano J. Alvarez, Yao Shen, Federico M. Giorgi et al. · 2016 · Nature Genetics · 1.0K citations
What is the proper way to apply the multiple comparison test?
Sangseok Lee, Dong Kyu Lee · 2018 · Korean journal of anesthesiology · 977 citations
Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimen...
Reading Guide
Foundational Papers
Start with Benjamini and Yekutieli (2001) for dependency FDR control, the most-cited (10532) extension of BH procedure essential for trial data. Follow with Wacholder et al. (2004) to understand false positive risks in molecular studies. Nuzzo (2014) contextualizes p-value pitfalls motivating FDR.
Recent Advances
Morris et al. (2019) details simulation evaluation of FDR methods for trials. Skrivankova et al. (2021) covers reporting in related MR studies needing multiple testing. Lee and Lee (2018) clarifies MCT application including FDR.
Core Methods
Benjamini-Hochberg sorts p-values, rejects if p(k) ≤ (k/m)q. Yekutieli weights by ∑1/i for dependence. Simulations generate data under truth (Morris 2019), compute empirical FDR/power curves.
How PapersFlow Helps You Research False Discovery Rate Control
Discover & Search
Research Agent uses citationGraph on Benjamini and Yekutieli (2001) to map 10532-cited dependency extensions, then findSimilarPapers for trial-specific adaptations. exaSearch queries 'FDR control genomics clinical trials dependence' to uncover 250M+ OpenAlex papers beyond lists. searchPapers with 'Benjamini-Hochberg imaging biomarkers' surfaces Wacholder et al. (2004) applications.
Analyze & Verify
Analysis Agent runs readPaperContent on Benjamini and Yekutieli (2001) to extract dependency formulas, then verifyResponse with CoVe against simulations. runPythonAnalysis simulates FDR power curves via NumPy/pandas for BH vs BY procedures. GRADE grading assesses evidence strength for trial method comparisons from Morris et al. (2019).
Synthesize & Write
Synthesis Agent detects gaps in dependency-robust FDR via contradiction flagging across Benjamini and Yekutieli (2001) and recent trials. Writing Agent uses latexEditText to draft method comparisons, latexSyncCitations for 10+ papers, and latexCompile for trial simulation reports. exportMermaid visualizes FDR procedure flowcharts from code analyses.
Use Cases
"Simulate power of Benjamini-Hochberg vs Yekutieli under dependence for 1000-gene trial"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (NumPy Monte Carlo sim, matplotlib power curves) → researcher gets CSV of FDR/power metrics vs sample size.
"Write LaTeX appendix comparing FDR methods for imaging biomarker paper"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Benjamini 2001 et al.) + latexCompile → researcher gets PDF-ready appendix with tables/figures.
"Find GitHub repos implementing adaptive FDR for clinical trials"
Research Agent → paperExtractUrls (Morris 2019) → paperFindGithubRepo → githubRepoInspect → researcher gets vetted code for trial simulations with READMEs.
Automated Workflows
Deep Research workflow conducts systematic FDR review: searchPapers (50+ Benjamini extensions) → citationGraph → DeepScan (7-step CoVe-verified analysis with GRADE) → structured report on trial applications. Theorizer generates hypotheses on dependence-robust FDR from Wacholder (2004) + simulations via runPythonAnalysis. DeepScan evaluates method power with checkpoints verifying Benjamini-Yekutieli assumptions against trial data.
Frequently Asked Questions
What defines False Discovery Rate control?
FDR controls the expected proportion of false positives among rejected nulls in multiple testing. Benjamini-Hochberg (1995, extended 2001) sorts p-values and rejects sequentially at α/m rates. Applies to clinical trial genomics/imaging with thousands of hypotheses.
What are core FDR methods?
Benjamini-Hochberg assumes independence; Benjamini-Yekutieli (2001) adjusts for general dependence via ∑1/i factor. Storey’s q-value adapts for estimated π0 proportion of true nulls. Evaluations use simulations per Morris et al. (2019).
What are key papers on FDR?
Benjamini and Yekutieli (2001, 10532 citations) controls FDR under dependency. Wacholder et al. (2004, 1770 citations) assesses false positives in epidemiology. Nuzzo (2014, 1547 citations) critiques statistical errors driving FDR needs.
What open problems exist in FDR?
Power loss under strong dependence lacks optimal solutions beyond Benjamini-Yekutieli conservatism. Sparse signals in trials challenge detection. Realistic simulations for heterogeneous trial data remain underdeveloped (Morris et al. 2019).
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