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<p>In biomedical research, predetermining the appropriate number of samples is essential to ensure the validity of findings, optimize resource allocation, and support meaningful scientific discovery. Accurate sample size estimation is particularly critical in complex study designs, such as those found in metabolomics, including metabolic phenotyping and integrative metabolomics. However, this task remains challenging due to the high dimensionality and variability inherent in metabolomics data. In recent years, efforts have been made to devise techniques and applications that could assist in designing and implementing metabolomics studies. Despite these efforts, a comprehensive evaluation of existing approaches is lacking, limiting the guidance available to researchers and potentially hindering progress in the field. To address this gap, a systematic literature review was conducted, mining two major scholarly databases (Scopus and MEDLINE <i>via</i> PubMed) and identifying twenty relevant studies. This review aims to provide an overview of the currently available methodologies for conducting a priori sample size calculations and power analyses in metabolomics, while also highlighting ongoing challenges and outlining directions for future research.</p>

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A priori sample size determination and power analysis in metabolic phenotyping and integrative metabolomics: an application framework based on a systematic review of literature

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Nicola Luigi Bragazzi,
  • Sara Dobani,
  • José Fernando Rinaldi de Alvarenga,
  • Cristiana Mignogna,
  • Daniele Del Rio,
  • Pedro Mena

摘要

In biomedical research, predetermining the appropriate number of samples is essential to ensure the validity of findings, optimize resource allocation, and support meaningful scientific discovery. Accurate sample size estimation is particularly critical in complex study designs, such as those found in metabolomics, including metabolic phenotyping and integrative metabolomics. However, this task remains challenging due to the high dimensionality and variability inherent in metabolomics data. In recent years, efforts have been made to devise techniques and applications that could assist in designing and implementing metabolomics studies. Despite these efforts, a comprehensive evaluation of existing approaches is lacking, limiting the guidance available to researchers and potentially hindering progress in the field. To address this gap, a systematic literature review was conducted, mining two major scholarly databases (Scopus and MEDLINE via PubMed) and identifying twenty relevant studies. This review aims to provide an overview of the currently available methodologies for conducting a priori sample size calculations and power analyses in metabolomics, while also highlighting ongoing challenges and outlining directions for future research.