On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.
On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.
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关于个性化医疗的因果推论：隐藏的因果假设如何导致关于 D 值的错误因果断言。
DOI：
10.1080/00031305.2019.1575771
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Robins,JamesM
中科院分区：
文献类型：
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作者：
Greenland,Sander;Fay,MichaelP;Brittain,EricaH;Shih,JoannaH;Follmann,DeanA;Gabriel,ErinE;Robins,JamesM
个性化医疗询问的是一种新的治疗方法是否会帮助特定的患者，而不是它是否会改善人群的平均反应。如果没有一个因果模型来区分这些问题，解释错误就会出现。这些错误在Demarko的一篇文章中被发现，该文章推荐了“D值”，即从新治疗组中随机选择的人比从对照治疗组中随机选择的人具有更高的结果值的概率。摘要指出“D值有一个明确的解释，即治疗后病情恶化的患者比例”，随后出现了类似的断言。我们证明这些陈述是不正确的，因为它们需要对潜在结果进行假设，这些假设既不能在随机实验中检验，也不能在一般情况下合理。如果（如预期）这些结果是相关的，D值将不等于治疗后恶化的患者比例。潜在结果的独立性是不现实的，并消除了任何个性化的治疗效果;依赖性，D值甚至可以暗示治疗优于对照，尽管大多数患者受到治疗的伤害。因此，D值对个性化医疗具有误导性。为了防止误解，我们建议将因果模型纳入基础统计教育。
Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “TheD-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. TheD-value willnotequal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminatesanypersonalized treatment effects; with dependence, theD-value can even imply treatment is better than controleven though most patients are harmed by the treatment. Thus,D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.