Credible ecological inference for medical decisions with personalized risk assessment

• 成果类型: Article
• 署名作者: Manski, Charles F.
• 署名单位: Northwestern University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE778
• 发表日期: 2018
• 页码: 541-569
• 关键词: Ambiguity; clinical judgment; partial identification; patient care

摘要

This paper studies an identification problem that arises when clinicians seek to personalize patient care by predicting health outcomes conditional on observed patient covariates. Let y be an outcome of interest and let (x=k, w=j) be observed patient covariates. Suppose a clinician wants to choose a care option that maximizes a patient's expected utility conditional on the observed covariates. To accomplish this, the clinician needs to know the conditional probability distribution P(y|x=k, w=j). It is common to have a trustworthy evidence-based risk assessment that predicts y conditional on a subset of the observed covariates, say x, but not conditional on (x, w). Then the clinician knows P(y|x=k) but not P(y|x=k, w=j). Research on the ecological inference problem studies partial identification of P(y|x, w) given knowledge of P(y|x) and P(w|x). Combining this knowledge with structural assumptions yields tighter conclusions. A psychological literature comparing actuarial predictions and clinical judgments has concluded that clinicians should not attempt to subjectively predict patient outcomes conditional on covariates that are not utilized in evidence-based risk assessments. I argue that formalizing clinical judgment through analysis of the identification problem can improve risk assessments and care decisions.