1. The longitudinal study of populations is a core tool for understanding ecological and evolutionary processes. Long‐term studies typically collect samples repeatedly over individual lifetimes and across generations. These samples are then analysed in batches (e.g. qPCR plates) and clusters (i.e. group of batches) over time in the laboratory. However, these analyses are constrained by cross‐classified data structures introduced biologically or through experimental design. The separation of biological variation from the confounding among‐batch and among‐cluster variation is crucial, yet often ignored.
2. The commonly used approaches to structuring samples for analysis, sequential and randomization, generate bias due to the non‐independence between time of collection and the batch and cluster they are analysed in. We propose a new sample structuring strategy, called slicing, designed to separate confounding among‐batch and among‐cluster variation from biological variation. Through simulations, we tested the statistical power and precision to detect within‐individual, between‐individual, year and cohort effects of this novel approach.
3. Our slicing approach, whereby recently and previously collected samples are sequentially analysed in clusters together, enables the statistical separation of collection time and cluster effects by bridging clusters together, for which we provide a case study. Our simulations show, with reasonable slicing width and angle, similar precision and similar or greater statistical power to detect year, cohort, within‐ and between‐individual effects when samples are sliced across batches, compared with strategies that aggregate longitudinal samples or use randomized allocation.
4. While the best approach to analysing long‐term datasets depends on the structure of the data and questions of interest, it is vital to account for confounding among‐cluster and batch variation. Our slicing approach is simple to apply and creates the necessary statistical independence of batch and cluster from environmental or biological variables of interest. Crucially, it allows sequential analysis of samples and flexible inclusion of current data in later analyses without completely confounding the analysis. Our approach maximizes the scientific value of every sample, as each will optimally contribute to unbiased statistical inference from the data. Slicing thereby maximizes the power of growing biobanks to address important ecological, epidemiological and evolutionary questions.

中文翻译：

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切片：构建样本以进行长期研究分析的可持续方法

1. 人口的纵向研究是了解生态和进化过程的核心工具。长期研究通常会在整个生命周期和各个世代中反复收集样本。然后在实验室中随时间推移分批分析这些样品（例如qPCR板）和簇（即一批批次）。然而，这些分析受到生物学或通过实验设计引入的交叉分类数据结构的限制。将生物变异与批间变异和集群间变异混淆分开是至关重要的，但常常被忽略。
2. 常用的结构化分析，顺序和随机化样本的方法，由于收集时间与分析样本的批次和簇之间的非独立性而产生偏差。我们提出了一种新的样本结构化策略，称为切片，旨在分离批次间和群集间变异与生物学变异之间的混淆。通过模拟，我们测试了统计能力和准确性，以检测这种新颖方法在个体内部，个体之间，年份和同类人群中的作用。
3. 我们的切片方法将最近和以前收集的样本按聚类顺序分析，从而通过将聚类连接在一起将收集时间和聚类效果进行统计分离，为此我们提供了一个案例研究。我们的模拟显示，以合理的切片宽度和角度，与将纵向样本汇总或使用随机分配的策略相比，当将样本分为多个批次进行切片时，可以检测出年份，同类，个体内部和个体之间的影响，并且具有相似的精度和相似或更强的统计能力。
4. 尽管分析长期数据集的最佳方法取决于数据的结构和感兴趣的问题，但考虑群集之间和批次之间的混淆是至关重要的。我们的切片方法易于应用，并且可以根据所需的环境或生物变量创建批次和簇的必要统计独立性。至关重要的是，它允许对样本进行顺序分析，并在以后的分析中灵活地包含当前数据，而不会完全混淆分析。我们的方法最大程度地提高了每个样本的科学价值，因为每个样本将最佳地促进数据的无偏统计推断。因此，切片可以最大程度地发挥生长中的生物库解决重要的生态，流行病学和进化问题的能力。