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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

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Related Experiment Video

Updated: May 8, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Data-driven sample size determination for metabolic phenotyping studies.

Benjamin J Blaise1

  • 1Hospices Civils de Lyon, Département de réanimation néonatale et néonatalogie, Hôpital Femme Mère Enfant, 59 bd Pinel, Bron Cedex, Bron 69677, France.

Analytical Chemistry
|August 27, 2013
PubMed
Summary
This summary is machine-generated.

Determining the right sample size for metabolic phenotyping studies is crucial for biomarker discovery. This study presents a data-driven method using NMR data to optimize sample size for reliable results.

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Last Updated: May 8, 2026

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Area of Science:

  • Biomedical Research
  • Translational Medicine
  • Metabolomics

Background:

  • Sample size determination is critical for medical study design, impacting statistical power and cost.
  • Metabolic phenotyping, a hypothesis-free approach, identifies metabolic rearrangements and potential biomarkers.
  • Challenges exist in sample size calculation for metabolic phenotyping due to the lack of a priori targets.

Purpose of the Study:

  • Introduce a data-driven approach for sample size determination in metabolic phenotyping studies.
  • Provide a method to optimize sample size for biomarker discovery and metabolic exploration.
  • Enhance the reliability and efficiency of metabolic phenotyping research.

Main Methods:

  • Utilized nuclear magnetic resonance (NMR) spectra from a small cohort.
  • Employed statistical recoupling of variables (SRV) to identify metabolic NMR variables.
  • Generated larger datasets using Kernel density estimation and assessed significance with Benjamini-Yekutieli correction.
  • Evaluated model robustness using receiver operating characteristic analysis and cross-validation.

Main Results:

  • Developed a data-driven method for sample size determination in metabolic phenotyping.
  • Demonstrated the ability to identify statistically significant metabolic variations.
  • Showcased optimal sample size determination based on study objectives (biomarker discovery vs. metabolic exploration).

Conclusions:

  • The proposed data-driven method effectively determines optimal sample sizes for metabolic phenotyping.
  • This approach enhances the statistical power and reliability of biomarker discovery.
  • Facilitates efficient experimental design in translational metabolic research.