S1_Code_Main_Analysis.R

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 ischaemic stroke. First, by quantifying inter-individual 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 inter-individual variability), as well as those where there is high inter-individual variability in response; for these latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimise variability in phenotypic outcomes, we can motivate the shift towards heterogenization and improve both the replicability and generalizability of preclinical research.

科研成果的可重复性始终是科学界愈发关注的核心议题。长期以来，学界普遍认为实验标准化是提升研究可重复性的关键，但这一认知近期受到挑战：有研究发现，缩小研究内部的变异程度反而会催生仅适用于特定实验室的特异性结果，这类结果难以被复现。与之相对的一种新兴研究范式是主动引入异质性，即实验设计的异质化（heterogenization）。本研究针对缺血性卒中（ischaemic stroke）的对照与实验动物模型中观测到的表型结果（phenotypic outcomes）变异度开展荟萃分析（meta-analysis），探索异质化研究框架下的全新视角。其一，通过量化对照组的个体间变异度，我们证实疾病状态（梗死体积，infarct volume）的异质性水平会因实验方法（如疾病造模手段与模型选择）的不同而存在差异。我们提出，此类方法可通过在评估治疗效果的参考组中构建多样化且具有代表性的基线疾病状态分布，从而提升研究的可重复性。其二，我们展示了如何通过荟萃分析同时评估治疗效果的有效性与稳定性，即平均效应量与个体间变异度。我们筛选出两类疗法：一类兼具治疗有效性且可推广至人群层面，即个体间变异度较低的疗法；另一类则在治疗响应层面存在较高的个体间变异度，此类疗法若要转化至临床场景，可能需要进行精细化调整。我们主张，与其刻意最小化表型结果的变异度，不如主动接纳这类变异，借此推动研究范式向异质化转型，进而全面提升临床前研究（preclinical research）的可重复性与推广性。