Essentially all models are wrong, but some are useful

Embracing Biological and Methodological Variance in a New Approach to Pre-Clinical Stroke Testing

High-profile failures in stroke clinical trials have discouraged clinical translation of neuroprotectants. While there are several plausible explanations for these failures, we believe that the fundamental problem is the way clinical and pre-clinical studies are designed and analyzed for heterogeneous disorders such as stroke due to innate biological and methodological variability that current methods cannot capture. Recent efforts to address pre-clinical rigor and design, while important, are unable to account for variability present even in genetically homogenous rodents. Indeed, efforts to minimize variability may lessen the clinical relevance of pre-clinical models. We propose a new approach that recognizes the important role of baseline stroke severity and other factors in influencing outcome. Analogous to clinical trials, we propose reporting baseline factors that influence outcome and then adapting for the pre-clinical setting a method developed for clinical trial analysis where the influence of baseline factors is mathematically modeled and the variance quantified. A new therapy's effectiveness is then evaluated relative to the pooled outcome variance at its own baseline conditions. In this way, an objective threshold for robustness can be established that must be overcome to suggest its effectiveness when expanded to broader populations outside of the controlled environment of the PI's laboratory. The method is model neutral and subsumes sources of variance as reflected in baseline factors such as initial stroke severity. We propose that this new approach deserves consideration for providing an objective method to select agents worthy of the commitment of time and resources in translation to clinical trials.