Dataset Information

MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.

ABSTRACT:

Background

Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely done in metabolomics, currently existing sample size estimation approaches which rely on pilot data can not be applied.

Results

In this article, an analysis based approach called MetSizeR is developed to estimate sample size for metabolomic experiments even when experimental pilot data are not available. The key motivation for MetSizeR is that it considers the type of analysis the researcher intends to use for data analysis when estimating sample size. MetSizeR uses information about the data analysis technique and prior expert knowledge of the metabolomic experiment to simulate pilot data from a statistical model. Permutation based techniques are then applied to the simulated pilot data to estimate the required sample size.

Conclusions

The MetSizeR methodology, and a publicly available software package which implements the approach, are illustrated through real metabolomic applications. Sample size estimates, informed by the intended statistical analysis technique, and the associated uncertainty are provided.

SUBMITTER: Nyamundanda G

PROVIDER: S-EPMC4222287 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Publications

MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.

Nyamundanda Gift G Gormley Isobel Claire IC Fan Yue Y Gallagher William M WM Brennan Lorraine L

BMC bioinformatics 20131121

<h4>Background</h4>Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely done in metabolomics, currently existing sample size estimation approaches which rely on pilot data can not be applied.<h4>Results</h4>In this article, an analysis based approach called MetSizeR is developed to estimate sample size for metabolomic expe ...[more]

PMID: 24261687

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Project description:BACKGROUND:Value of information is now recognized as a reference method in the decision process underpinning cost-effectiveness evaluation. The expected value of perfect information (EVPI) is the expected value from completely reducing the uncertainty surrounding the cost-effectiveness of an innovative intervention. Among sample size calculation methods used in cost-effectiveness studies, only one is coherent with this decision framework. It uses a Bayesian approach and requires data of a pre-existing cost-effectiveness study to derive a valid prior EVPI. When evaluating the cost-effectiveness of innovations, no observed prior EVPI is usually available to calculate the sample size. We here propose a sample size calculation method for cost-effectiveness studies, that follows the value of information theory, and, being frequentist, can be based on assumptions if no observed prior EVPI is available. METHODS:The general principle of our method is to define the sampling distribution of the incremental net monetary benefit (?B), or the distribution of ?B that would be observed in a planned cost-effectiveness study of size n. Based on this sampling distribution, the EVPI that would remain at the end of the trial (EVPIn) is estimated. The optimal sample size of the planned cost-effectiveness study is the n for which the cost of including an additional participant becomes equal or higher than the value of the information gathered through this inclusion. RESULTS:Our method is illustrated through four examples. The first one is used to present the method in depth and describe how the sample size may vary according to the parameters' value. The three other examples are used to illustrate in different situations how the sample size may vary according to the ceiling cost-effectiveness ratio, and how it compares with a test statistic-based method. We developed an R package (EBASS) to run these calculations. CONCLUSIONS:Our sample size calculation method follows the value of information theory that is now recommended for analyzing and interpreting cost-effectiveness data, and sets the size of a study that balances its cost and the value of its information.

Dataset Information

MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.

Background

Results

Conclusions

Publications

MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.

Similar Datasets

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