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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

7.1K
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...
7.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Sample size calculation in metabolic phenotyping studies.

Elise Billoir, Vincent Navratil, Benjamin J Blaise

    Briefings in Bioinformatics
    |January 21, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Determining the correct sample size is crucial for metabolic phenotyping studies. The Data-driven Sample size Determination (DSD) algorithm optimizes this by using pilot data to predict the ideal number of samples for biomarker discovery.

    Keywords:
    chemometricsmetabolic phenotypingsample size determination

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    Area of Science:

    • Metabolomics
    • Biomedical Research
    • Statistical Genetics

    Background:

    • Sample size determination is critical for the validity and cost-effectiveness of biomedical studies.
    • Metabolic phenotyping studies face unique challenges in sample size calculation due to complex statistical frameworks and hypothesis-free approaches.
    • No standardized method previously existed for sample size estimation in metabolic phenotyping.

    Purpose of the Study:

    • To review existing sample size estimation procedures for metabolic phenotyping.
    • To introduce an automated implementation of the Data-driven Sample size Determination (DSD) algorithm.
    • To provide an optimized method for determining sample size in metabolic phenotyping studies.

    Main Methods:

    • The Data-driven Sample size Determination (DSD) algorithm utilizes data from a small pilot cohort to generate an expanded dataset.
    • Statistical recoupling of variables identifies key metabolic variables, with intensity distributions estimated via Kernel smoothing or log-normal density fitting.
    • The Benjamini-Yekutieli correction is employed to evaluate statistically significant metabolic variations across various dataset sizes.

    Main Results:

    • The DSD algorithm enables optimized sample size determination for metabolic phenotyping.
    • It supports both biomarker discovery (identifying at least one significant variation) and metabolic exploration (maximizing significant variations).
    • An automated implementation of DSD is available for MATLAB and GNU Octave.

    Conclusions:

    • The DSD algorithm offers a standardized and optimized approach to sample size determination in metabolic phenotyping.
    • This tool facilitates more efficient experimental design, reduces costs, and enhances the potential for significant discoveries.
    • The DSD toolbox provides a practical solution for researchers in the field.