Embrace heterogeneity to improve reproducibility: A perspective from meta-analysis of variation in preclinical research

The reproducibility of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets reproducibility has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that are irreproducible. 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 samount 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 reproducibility 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 reproducibility and generalizability of preclinical research.