Abstract Providing terminally ill patients with access to experimental treatments, as allowed by recent “right to try” laws and “expanded access” programs, poses a variety of ethical questions. While practitioners and investigators may assume it is impossible to learn the effects of these treatment without randomized trials, this paper describes a simple tool to estimate the effects of these experimental treatments on those who take them, despite the problem of selection into treatment, and without assumptions about the selection process. The key assumption is that the average outcome, such as survival, would remain stable over time in the absence of the new treatment. Such an assumption is unprovable, but can often be credibly judged by reference to historical data and by experts familiar with the disease and its treatment. Further, where this assumption may be violated, the result can be adjusted to account for a hypothesized change in the non-treatment outcome, or to conduct a sensitivity analysis. The method is simple to understand and implement, requiring just four numbers to form a point estimate. Such an approach can be used not only to learn which experimental treatments are promising, but also to warn us when treatments are actually harmful – especially when they might otherwise appear to be beneficial, as illustrated by example here. While this note focuses on experimental medical treatments as a motivating case, more generally this approach can be employed where a new treatment becomes available or has a large increase in uptake, where selection bias is a concern, and where an assumption on the change in average non-treatment outcome over time can credibly be imposed.

中文翻译：

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尽管自我选择，估计新疗法的因果效应：实验医学治疗案例

摘要 在最近的“尝试权”法律和“扩大获取”计划允许的情况下，为绝症患者提供实验性治疗会带来各种伦理问题。虽然从业者和研究人员可能认为没有随机试验就不可能了解这些治疗的效果，但本文描述了一种简单的工具来估计这些实验治疗对那些接受它们的人的影响，尽管存在选择治疗的问题，并且没有关于选择过程的假设。关键假设是，在没有新治疗的情况下，平均结果（例如生存率）将随着时间的推移保持稳定。这种假设是无法证实的，但通常可以通过参考历史数据和熟悉该疾病及其治疗的专家来可靠地判断。更多，在可能违反此假设的情况下，可以调整结果以说明非治疗结果的假设变化，或进行敏感性分析。该方法易于理解和实现，只需要四个数字就可以形成一个点估计。这种方法不仅可以用来了解哪些实验性治疗是有希望的，还可以在治疗实际上有害时警告我们——尤其是当它们看起来可能有益时，如这里的示例所示。虽然本说明侧重于作为激励案例的实验性医学治疗，但更普遍地，这种方法可用于新治疗可用或吸收率大幅增加的情况，其中选择偏差是一个问题，