论文标题
通过对二进制亚组的试验分析对个性化治疗反应的部分识别
Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups
论文作者
论文摘要
医学期刊遵守了一种报告实践，该实践严重限制了已发表的试验结果的有用性。医疗决策者通常观察许多患者协变量，并寻求使用此信息来个性化治疗选择。然而，试验结果的标准摘要只有将受试者分为广泛的亚组，通常分为二进制类别。鉴于这种报告实践，我们研究了长期平均治疗结果的推断问题E [y（t）| X]，其中t是一种治疗，y（t）是一种治疗结果，协变量矢量X具有长度K，每个组件都是二进制变量。可用的数据是{e [y（t）| xk = 0]，e [y（t）| xk = 1]，p（xk）}，k = 1，。 。 。 ，k在期刊文章中报道。我们表明，报告的试验结果部分识别{e [y（t）| x]，p（x）}。说明性计算表明，期刊文章中试验结果的摘要可能仅表示长期均值结果的广泛界限。如果人们可以将报告的试验结果与具有识别能力的可靠假设（例如有限变化的假设）相结合，则可以实际收紧推断。
Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically into binary categories. Given this reporting practice, we study the problem of inference on long mean treatment outcomes E[y(t)|x], where t is a treatment, y(t) is a treatment outcome, and the covariate vector x has length K, each component being a binary variable. The available data are estimates of {E[y(t)|xk = 0], E[y(t)|xk = 1], P(xk)}, k = 1, . . . , K reported in journal articles. We show that reported trial findings partially identify {E[y(t)|x], P(x)}. Illustrative computations demonstrate that the summaries of trial findings in journal articles may imply only wide bounds on long mean outcomes. One can realistically tighten inferences if one can combine reported trial findings with credible assumptions having identifying power, such as bounded-variation assumptions.
