Subtopic Deep Dive
Multiple Testing Procedures
Research Guide
What is Multiple Testing Procedures?
Multiple testing procedures are statistical methods that control error rates such as familywise error rate (FWER) or false discovery rate (FDR) when performing multiple hypothesis tests simultaneously in clinical trials.
These procedures include step-up/down methods, closed testing, and graphical approaches to manage multiplicity in confirmatory trials with multiple endpoints. Benjamini and Hochberg (1995) introduced FDR control, extended by Benjamini and Yekutieli (2001, 10532 citations) to dependent tests. Feise (2002, 1248 citations) questioned routine p-value adjustment for multiple outcomes.
Why It Matters
Multiple testing procedures prevent inflated Type I error rates in clinical trials testing multiple endpoints, ensuring reliable evidence for drug approvals. Benjamini and Yekutieli (2001) enable FDR control under dependency, applied in genomics-informed trials like warfarin dosing (Takeuchi et al., 2009, 633 citations; Caldwell et al., 2008, 518 citations). Feise (2002) guides primary endpoint selection, while Lee and Lee (2018, 977 citations) clarify post-hoc test application, safeguarding trial integrity against false positives.
Key Research Challenges
Dependency Handling
Test statistics in clinical trials often exhibit dependence, complicating FWER or FDR control. Benjamini and Yekutieli (2001) provide conservative adjustments for arbitrary dependencies. Challenges persist in deriving tight bounds for complex correlation structures.
Power Optimization
Procedures must balance error control with statistical power for detecting true effects across multiple endpoints. Feise (2002) highlights trade-offs in primary vs. multiple outcomes. Colquhoun (2014, 694 citations) warns of low power inflating false discovery risks.
Graphical Method Adaptation
Graphical procedures allocate alpha across endpoints but require validation under trial adaptations. Chen et al. (2017, 696 citations) introduce general adjustments. Ensuring strong FWER control in adaptive designs remains unresolved.
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...
Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims: draft guidance
For some treatment effects, the patient is the only source of data. For example, pain intensity and pain relief are the fundamental measures used in the development of analgesic products. There are no observable or physical measures for these concepts. · 2006 · Health and Quality of Life Outcomes · 2.8K citations
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...
Do multiple outcome measures require p-value adjustment?
Ronald J. Feise · 2002 · BMC Medical Research Methodology · 1.2K citations
Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures m...
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...
Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods
Janus Christian Jakobsen, Jørn Wetterslev, Per Winkel et al. · 2014 · BMC Medical Research Methodology · 703 citations
A general introduction to adjustment for multiple comparisons
Shiyi Chen, Zhe Feng, Yi Xiaolian · 2017 · Journal of Thoracic Disease · 696 citations
In experimental research a scientific conclusion is always drawn from the statistical testing of hypothesis, in which an acceptable cutoff of probability, such as 0.05 or 0.01, is used for decision...
Reading Guide
Foundational Papers
Start with Benjamini and Yekutieli (2001) for FDR under dependency (10532 citations), then Feise (2002) for clinical trial multiplicity debates—establishes core error rate concepts.
Recent Advances
Lee and Lee (2018, 977 citations) on proper multiple comparison application; Chen et al. (2017, 696 citations) for general adjustment introductions.
Core Methods
Step-up/down (Benjamini-Hochberg), closed testing, graphical procedures; simulations for dependency via Benjamini and Yekutieli (2001).
How PapersFlow Helps You Research Multiple Testing Procedures
Discover & Search
Research Agent uses searchPapers and exaSearch to find Benjamini and Yekutieli (2001) on FDR under dependency, then citationGraph reveals 10532 citing papers including clinical applications, while findSimilarPapers uncovers Feise (2002) for outcome adjustment debates.
Analyze & Verify
Analysis Agent applies readPaperContent to extract FDR proofs from Benjamini and Yekutieli (2001), verifies power claims via runPythonAnalysis simulating dependent tests with NumPy, and uses verifyResponse (CoVe) with GRADE grading to assess evidence quality in Feise (2002) recommendations.
Synthesize & Write
Synthesis Agent detects gaps in dependency handling post-Benjamini and Yekutieli (2001), flags contradictions between Feise (2002) and Lee and Lee (2018), while Writing Agent uses latexEditText, latexSyncCitations for trial multiplicity sections, and latexCompile for publication-ready reports.
Use Cases
"Simulate FDR control under correlated endpoints in oncology trials"
Research Agent → searchPapers('FDR clinical trials') → Analysis Agent → runPythonAnalysis (NumPy sim of Benjamini-Yekutieli procedure on 10 correlated p-values) → matplotlib power curve output.
"Write LaTeX section on step-up procedures for multiple endpoints"
Synthesis Agent → gap detection (Benjamini 2001 + Feise 2002) → Writing Agent → latexEditText (draft text) → latexSyncCitations → latexCompile → PDF with graphical alpha allocation diagram.
"Find code implementations of closed testing procedures"
Research Agent → paperExtractUrls (Lee and Lee 2018) → Code Discovery → paperFindGithubRepo → githubRepoInspect → R/Python scripts for closed testing in trial data.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers(50+ multiplicity papers) → citationGraph(Benjamini cluster) → GRADE synthesis on FWER vs FDR. DeepScan applies 7-step analysis with CoVe checkpoints to verify Colquhoun (2014) p-value critiques against trial data. Theorizer generates hypotheses on graphical methods from Feise (2002) and Chen et al. (2017).
Frequently Asked Questions
What defines multiple testing procedures?
Statistical methods controlling FWER or FDR across simultaneous hypothesis tests, as in Benjamini and Yekutieli (2001) for dependent cases.
What are key methods?
FDR via Benjamini-Hochberg step-up, closed testing, and graphical alpha allocation; extended to dependencies by Benjamini and Yekutieli (2001).
What are seminal papers?
Benjamini and Yekutieli (2001, 10532 citations) on FDR dependency; Feise (2002, 1248 citations) on outcome adjustment necessity.
What open problems exist?
Tight power-optimized controls under complex dependencies and adaptive designs; balancing FDR with clinical significance per Jakobsen et al. (2014).
Research Statistical Methods in Clinical Trials with AI
PapersFlow provides specialized AI tools for Mathematics researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Paper Summarizer
Get structured summaries of any paper in seconds
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Physics & Mathematics use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Multiple Testing Procedures with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Mathematics researchers