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Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
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Sequential interim analyses of survival data in DNA microarray experiments.

Andreas Leha1, Tim Beissbarth, Klaus Jung

  • 1Department of Medical Statistics, University Medical Center Göttingen, D-37099 Göttingen, Germany.

BMC Bioinformatics
|April 30, 2011
PubMed
Summary
This summary is machine-generated.

Interim analyses in microarray survival studies allow for early stopping and efficient biomarker discovery. This research evaluates false discovery rates and power rates, enabling better sample size planning and faster research conclusions.

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Area of Science:

  • Biostatistics
  • Genomics
  • Translational Oncology

Background:

  • Identifying biomarkers for therapy response and survival is crucial in severe diseases like cancer.
  • Microarray studies correlate gene expression in pretherapeutic samples with patient survival times.
  • Interim analyses are desirable for early study termination or validation experiments, especially in long-term studies.

Purpose of the Study:

  • To examine false discovery rates and power rates in microarray experiments during interim analyses of survival studies.
  • To evaluate early stopping criteria based on interim results.
  • To derive and compare a new estimator for average power rate.

Main Methods:

  • Simulation study to assess false discovery rates and power rates under interim analysis scenarios.
  • Evaluation of early stopping based on achieved average power rate.
  • Comparison of a novel power rate estimator with existing methods.

Main Results:

  • Pre-specified false discovery rates were maintained in interim analyses without needing classical reduced levels.
  • Average power rates increased with interim analyses, facilitating early study cessation.
  • The new power rate estimator showed comparable performance to existing estimators.

Conclusions:

  • Interim analyses of microarray experiments support early stopping of long-term survival studies.
  • A developed simulation framework, available as the R package 'SurvGenesInterim', aids in sample size planning for such designs.