[go: up one dir, main page]
Change language
English Deutsch
Change currency
€ EUR £ GBP $ USD
Licensed Unlicensed Requires Authentication Published by De Gruyter June 15, 2004

Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives

  • Mark J. van der Laan , Sandrine Dudoit and Katherine S. Pollard
From the journal Statistical Applications in Genetics and Molecular Biology
https://doi.org/10.2202/1544-6115.1042

This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise error rate (FWER) can be augmented into procedures that (asymptotically) control tail probabilities for the number of false positives and the proportion of false positives among the rejected hypotheses. Specifically, given any procedure that (asymptotically) controls the FWER at level alpha, we propose simple augmentation procedures that provide (asymptotic) level-alpha control of: (i) the generalized family-wise error rate, i.e., the tail probability, gFWER(k), that the number of Type I errors exceeds a user-supplied integer k, and (ii) the tail probability, TPPFP(q), that the proportion of Type I errors among the rejected hypotheses exceeds a user-supplied value 0

Keywords: Adjusted p-value; asymptotic control; augmentation; false discovery rate; generalized family-wise error rate; multiple testing; null distribution; proportion of false positives; single-step; step-down; tail probability; Type I error rate.
Published Online: 2004-6-15

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

From the journal
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology
Volume 3 Issue 1
Submit manuscript

Journal and Issue

Articles in the same Issue

Article
Using Alpha Wisely: Improving Power to Detect Multiple QTL
Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Asymptotic Optimality of Likelihood-Based Cross-Validation
Using Importance Sampling to Improve Simulation in Linkage Analysis
Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?
Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma
Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates
Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate
Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects
Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments
Mammalian Genomes Ease Location of Human DNA Functional Segments but Not Their Description
On the Dependence Structure of Sequence Alignment Scores Calculated with Multiple Scoring Matrices
Increasing Power for Tests of Genetic Association in the Presence of Phenotype and/or Genotype Error by Use of Double-Sampling
A Method for Evaluating the Impact of Individual Haplotypes on Disease Incidence in Molecular Epidemiology Studies
Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment
MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data
Sparse Inverse of Covariance Matrix of QTL Effects with Incomplete Marker Data
Maximum Likelihood for Genome Phylogeny on Gene Content
Confidence Levels for the Comparison of Microarray Experiments
PLS Dimension Reduction for Classification with Microarray Data
Statistical Analysis of Genomic Tag Data
Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays
Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks
Validation and Discovery in Markov Models of Genetics Data
Making Sense of High-Throughput Protein-Protein Interaction Data
Reader's Reaction
Reader Reaction
Response to Foulkes and De Gruttola
Software Communication
BayesMendel: an R Environment for Mendelian Risk Prediction
Letter to the Editor
Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors
Downloaded on 16.9.2024 from https://www.degruyter.com/document/doi/10.2202/1544-6115.1042/html
Scroll to top button