Consistency in drug treatment outcomes across variability induced by occlusion methodologies.

For our second-order meta-regression, we first separated our rat infarct volume data by occlusion methods. For each occlusion method data, we conducted a MLMR to estimate heterogeneity (I²) in lnRR including our original random (study ID, effect size ID, and strain) and fixed effects (sex + drug treatment group). From our MLMR models, we extracted total I² of lnRR and from this calculated the heterogeneity statistic lnH. lnH is a preferable effect size for downstream analyses as it is unbounded and has a relatively well-defined standard error to act as a measure of its precision [61 in main text]. Using the square of the standard error of lnH as the sampling variance and lnH as our response variable, we then fit a second-order meta-regression using the lnCV estimates of each occlusion method as a fixed predictor and effect size ID as a random effect (σ²_Residual = 0.200). Unconditional estimates of lnCV were obtained from our MLMR models of methodological variability (S1 Table) described in our main text. Estimates and 95% credible intervals from this second-order MLMR model is reported below. Estimates with credible intervals that do not span zero are considered statistically significant. See S3 Fig for a line plot depicting the relationship between lnH and lnCV with the model fitted line. lnCV, log coefficient of variation; lnRR, log response ratio; MLMR, multilevel meta-regression.

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