Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research
Fig 3
Categorization of treatment effects based on mean efficacy (lnRR) and interindividual variability in efficacy (lnCVR).
Estimates (circles) represent unconditional (marginalized), treatment-specific means (lnRR), variability (lnCVR), and their 95% CIs (solid lines) obtained from MLMR models. Treatments that significantly reduce infarct volume (negative lnRR) without significantly affecting the variation are highlighted green, with citicoline indicated by a diamond as the only treatment to significantly reduce infarct volume and also have a negative point estimate of lnCVR. Treatments that significantly reduce infarct volume and increase interindividual variability (positive lnCVR) are highlighted blue. The effects of hypothermia (most negative and positive mean and variability estimates, respectively) and thrombolytics (which include the only regulatory-approved treatment) are highlighted in pink. Histograms show the relationship of the mean and variance in infarct volume between control (orange) and treatment (blue) groups in each quadrant of the graph. The data underlying this figure can be found at https://doi.org/10.6084/m9.figshare.14527317.v4. CI, credible interval; lnCVR, log coefficient of variation ratio; lnRR, log response ratio; MLMR, multilevel meta-regression.
doi: https://doi.org/10.1371/journal.pbio.3001009.g003