Seabrook Jamie A
Department of Epidemiology and Biostatistics, Western University, London, ON N6G 2M1, Canada.
Brescia School of Food and Nutritional Sciences, Western University, London, ON N6G 2V4, Canada.
Nutrients. 2025 Aug 18;17(16):2668. doi: 10.3390/nu17162668.
Robust statistical analysis is essential for advancing evidence-based nutrition research, particularly when investigating the complex relationships between dietary exposure and health outcomes. Multiple regression is a widely used analytical technique in nutrition studies due to its ability to control for confounding variables and assess multiple predictors simultaneously. However, the reliability, validity, and generalizability of findings from regression analyses depend heavily on having an appropriate sample size. Despite its importance, many published nutrition studies do not include formal sample size justifications or power calculations, leading to a high risk of Type II errors and reduced interpretability of results. This methodological review examines three commonly used approaches to sample size determination in multiple regression analysis: the rule of thumb, variance explained (R) method, and beta weights approach. Using a consistent hypothetical example, rather than empirical data, this paper illustrates how sample size recommendations can differ depending on the selected approach, highlighting the advantages, assumptions, and limitations of each. This review is intended as an educational resource to support methodological planning for applied researchers rather than to provide new empirical findings. The aim is to equip nutrition researchers with practical tools to optimize sample size decisions based on their study design, research objectives, and desired power. The rule of thumb offers a simple and conservative starting point, while the R method ties sample size to anticipated model performance. The beta weights approach allows for more granular planning based on the smallest effect of interest, offering the highest precision but requiring more detailed assumptions. By encouraging more rigorous and transparent sample size planning, this paper contributes to improving the reproducibility and interpretability of quantitative nutrition research.
稳健的统计分析对于推进循证营养研究至关重要，尤其是在调查饮食暴露与健康结果之间的复杂关系时。多元回归是营养研究中广泛使用的分析技术，因为它能够控制混杂变量并同时评估多个预测因素。然而，回归分析结果的可靠性、有效性和可推广性在很大程度上取决于是否有合适的样本量。尽管样本量很重要，但许多已发表的营养研究并未包含正式的样本量合理性说明或功效计算，这导致出现II类错误的风险很高，且结果的可解释性降低。本方法学综述探讨了多元回归分析中确定样本量的三种常用方法：经验法则、解释方差（R）法和β权重法。本文使用一个一致的假设示例，而非实证数据，来说明根据所选方法样本量建议可能会如何不同，突出每种方法的优点、假设和局限性。本综述旨在作为一种教育资源，以支持应用研究人员进行方法学规划，而非提供新的实证研究结果。目的是为营养研究人员提供实用工具，以便根据他们的研究设计、研究目标和所需功效来优化样本量决策。经验法则提供了一个简单而保守的起点，而R法将样本量与预期的模型性能联系起来。β权重法允许根据最小的感兴趣效应进行更细致的规划，提供最高的精度，但需要更详细的假设。通过鼓励更严格和透明的样本量规划，本文有助于提高定量营养研究的可重复性和可解释性。
