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Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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Updated: May 14, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Optimal False Discovery Rate Control for Dependent Data.

Jichun Xie1, T Tony Cai, John Maris

  • 1Department of Statistics, The Fox School of Business and Management, Temple University, jichun@temple.edu.

Statistics and Its Interface
|February 5, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal procedure for controlling the false discovery rate (FDR) with dependent test statistics. The proposed method effectively reduces false non-discovery rates, improving upon existing FDR control techniques in genetic studies.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genetics

Background:

  • Controlling the false discovery rate (FDR) is crucial in high-dimensional data analysis, especially when test statistics exhibit dependencies.
  • Existing methods often struggle to maintain optimal performance under dependent test statistics.

Purpose of the Study:

  • To develop an optimal procedure for false discovery rate control with dependent test statistics.
  • To propose a data-driven method that approximates the optimal procedure for multivariate normal data.
  • To evaluate the performance of the proposed method against existing FDR controlling procedures.

Main Methods:

  • Development of an optimal joint oracle procedure to minimize the false non-discovery rate (FNDR) under an FDR constraint.
  • Proposal of a data-driven marginal plug-in procedure for approximating the joint procedure.
  • Asymptotic optimality analysis for multivariate normal data with short-range dependent covariance structures.

Main Results:

  • The marginal plug-in procedure is shown to be asymptotically optimal for specific data structures.
  • Numerical simulations demonstrate effective FDR control and a reduced FNDR compared to p-value based methods.
  • Application to a neuroblastoma GWAS identified additional potentially associated genetic variants.

Conclusions:

  • The proposed marginal procedure offers an effective approach for FDR control with dependent data.
  • This method enhances the discovery of relevant genetic variants in complex association studies.
  • The procedure provides a valuable tool for statistical inference in genomics and other fields with dependent data.