Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research.
Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research.
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DOI：
10.1371/journal.pbio.3001009
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发表时间：
2021-05
期刊：
影响因子：
9.8
通讯作者：
Nakagawa S
中科院分区：
文献类型：
--
作者：
Usui T;Macleod MR;McCann SK;Senior AM;Nakagawa S
研究结果的可重复性已经引起了科学界越来越多的关注。长期以来，人们一直认为实验标准化会带来可重复性，但这一观点最近也受到了挑战，因为研究中可变性的减少可能导致无法复制的特殊实验室特定结果。另一种方法是故意引入异质性，即实验设计的“异质性”。在这里，我们在对照和实验动物模型中观察到的缺血性卒中表型结果的变异性的荟萃分析中探索了异质化程序的新视角。首先，通过量化对照组的个体间变异性，我们发现疾病状态（梗死体积）的异质性程度根据方法的不同而不同，例如在疾病诱导方法和疾病模型中。我们认为，这些方法可以通过在参考组中创建基线疾病状态的多样化和代表性分布来提高可重复性，并据此评估治疗效果。其次，我们说明了meta分析如何同时用于评估疗效和稳定性（即平均效应和个体间变异性）。我们确定了具有疗效并可推广到人群水平的治疗方法（即，低个体间变异性），以及那些在反应中存在高个体间变异性的治疗方法；对于这些，后期治疗转化为临床设置可能需要细微差别。我们认为，通过拥抱而不是寻求最小化表型结果的可变性，我们可以推动向异质化的转变，并提高临床前研究的可重复性和普遍性。一项对缺血性脑卒中对照和实验动物模型表型结果变异性的荟萃分析研究提供了新的视角，可以接受异质性，以提高临床前研究的可重复性和转译性。
The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as “heterogenization” of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research. A meta-analysis study of the variability in phenotypic outcomes in both control and experimental animal models of ischaemic stroke provides novel perspectives in which heterogeneity can be embraced to improve the reproducibility and translation of preclinical studies.
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