This viewpoint proposes integrating causal inference tools and counterfactual adjudication methods to address euthanasia in veterinary research, highlighting its impact on survival outcomes.
While essential for the ethical practice of veterinary medicine, euthanasia profoundly complicates research with a survival outcome. In particular, euthanasia can make it difficult to determine the extent to which a certain clinical sign, lab, or imaging finding is associated with poor prognosis since animals that die while receiving veterinary care are often euthanized rather than dying naturally. The reasons for euthanasia, however, may be dramatically different between patients. Some are euthanized because prognosis is considered extremely poor even with extensive treatment whereas others may be euthanized due to client financial limitations despite a reasonably good prognosis. In addition, when a clinician-scientist veterinarian believes a clinical finding is associated with poor survival, they may consciously or unconsciously influence clients to euthanize their animals. In effect, this could create – or artificially inflate the strength of – an association between that finding and patient survival. In this viewpoint, I will discuss the use of causal inference tools like directed acyclic graphs (DAGs) to identify the treating veterinarian’s belief about prognosis as a variable that mediates the effect of clinical findings on the probability of survival. Furthermore, use of counterfactual outcome adjudication committees in observational veterinary research with a survival outcome is proposed to estimate the probability that euthanized patients would have survived had they received additional treatment. By using both DAGs and these estimates of counterfactual survival probability to simulate outcomes for euthanized patients, investigators can estimate the causal effects of different clinical findings on probability of patient survival.
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Charles O. Cummings (2025) studied this question.
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