Rethinking animal attrition in preclinical research: expressing causal mechanisms of selection bias using directed acyclic graphs

Abstract

Animal attrition in preclinical experiments can introduce bias in the estimation of causal treatment effects, as surviving animals may not be representative of the entire study population. This can compromise the internal validity of the study, despite randomization at the outset. Directed Acyclic Graphs (DAGs) are commonly used tools to transparently visualize assumptions about the causal structure underlying observed data. By illustrating relationships between relevant variables, DAGs enable the detection of even less intuitive biases, and can thereby inform strategies for their mitigation. In this study, we present an illustrative causal model for preclinical stroke research, in which animal attrition induces a specific type of selection bias (i.e., collider stratification bias) due to the interplay of animal welfare, initial disease severity and negative side effects of treatment. Even when the treatment had no causal effect, our simulations revealed substantial bias across different scenarios. We show how researchers can potentially mitigate this bias in the analysis phase, even when only data from surviving animals are available, if knowledge of the underlying causal process that gave rise to the data is available. Collider stratification bias should be a concern in preclinical animal model studies with severe side effects and high post-randomization attrition.