This viewpoint proposes counterfactual outcome elicitation to address euthanasia effects on clinical findings, suggesting causal inference methods may help clarify associations.
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 due to perceived poor prognosis, others due to client financial limitations, and others for multifactorial reasons. 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. Then, I briefly discuss some statistical methods already in use to account for euthanasia in veterinary research, as well as their limitations. Lastly, I speculatively propose the use of expert elicitation to estimate counterfactual survival probability distributions (CSPD) for euthanized patients. By using these CSPDs to weight survival probability in euthanized patients and DAGs to identify and adjust for potential confounding, investigators can estimate the direct 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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