Inference on covariate effect types for treatment effectiveness in a randomized trial with a binary outcome
Inference on covariate effect types for treatment effectiveness in a randomized trial with a binary outcome
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DOI：
10.1177/1740774519828301
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发表时间：
2019-02
期刊：
影响因子：
2.7
通讯作者：
Y. Chiba
中科院分区：
文献类型：
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作者：
Y. Chiba
背景/目的一些随机临床试验试图建立协变量效应类型，以表明协变量是否具有预测性和/或预后性，以及终点评价。在这里，对于二元结局的病例，我们建议应根据四种类型的潜在反应评估协变量效应类型：激活型（总是），惰性型（从不），因果型和预防型反应者。方法我们引入了一个新的概念，协变量的影响类型不同于通常使用的“预测”和“预后”。我们通过检查协变量的两个亚组中每种反应类型中受试者的比例来总结协变量效应类型，并指出随着协变量水平的变化，分数是否增加，耗尽或中性。虽然这些比例一般不能确定，我们可以通过应用最近开发的贝叶斯方法得出的比例的后验分布。根据分布，如果两个亚组中因果应答者（如果接受治疗会应答，但如果不接受治疗则不会应答）的比例之间的差异为正，则我们认为协变量是“增强因果”的，而不是预测性的。类似地，如果激活应答者（无论其随机化治疗分配如何都会应答）的比例差异接近于零，我们会说协变量是“中性激活”，而不是说协变量不是预后性的。我们进一步描述了我们的方法和标准亚组分析之间的关系。我们将我们的方法应用于一项随机临床试验的数据，该试验比较了nivolumab和多西他赛治疗晚期非鳞状非小细胞肺癌受试者的数据;我们评估了PD-L1状态的协变量效应类型，其中PD-L1是活化T细胞表达的程序性死亡1（PD-1）受体的配体。当终点为总缓解率时，PD-L1阳性和阴性亚组中缓解类型受试者比例差异的后验分布产生的预期后验估计值为0.243（95%可信区间（CI）：0.094，0.374）和0.014（95% CI：-0.087，0.125）。因此，PD-L1状态是nivolumab有效性的增强原因，达到24.3%的程度，并且是中性激活的。结论我们的方法描述了协变量效应类型的响应类型，以及在多大程度上。在一项具有二元结局的随机临床试验中，我们的方法是对标准亚组或回归分析的潜在有价值的补充。
Background/aims Some randomized clinical trials seek to establish covariate effect types that indicate whether a covariate is predictive and/or prognostic, in addition to endpoint evaluation. Here, for a case with a binary outcome, we propose that the covariate effect type should be assessed in terms of four types of potential responses: activated- (always-), inert- (never-), causative-, and preventive-responder. Methods We introduce a new concept of covariate effect types differing from the commonly used “prediction” and “prognosis.” We summarize the covariate effect types by inspecting the proportions of subjects in each response type in two subgroups of a covariate, and indicate whether the fractions are augmented, depleted, or neutral as one changes the level of the covariate. Although these proportions cannot generally be identified, we can derive the posterior distributions of the proportions by applying a recently developed Bayesian method. On the basis of the distributions, we would say that the covariate is “augmented-causative” if the difference between the proportions of causative-responders (who would respond if they received the treatment but would not if they did not) in two subgroups is positive, rather than that it is predictive. Similarly, we would say that the covariate is “neutral-activated” if the difference in the proportion of activated-responders (who would respond regardless of their randomized treatment assignment) is close to zero, rather than saying that the covariate is not prognostic. We further describe the relationship between our approach and standard subgroup analysis. Results We applied our approach to data from a randomized clinical trial comparing nivolumab and docetaxel for subjects with advanced nonsquamous non-small-cell lung cancer; we assessed the covariate effect type of PD-L1 status, where PD-L1 is a ligand of the programmed death 1 (PD-1) receptor expressed by activated T cells. When the endpoint was the overall response rate, the posterior distributions for the differences between the proportions of subjects in response types in the PD-L1-positive and negative subgroups yielded an expected-a-posteriori estimate of 0.243 (95% credible interval (CI): 0.094, 0.374) for causative-responders and 0.014 (95% CI: −0.087, 0.125) for activated-responders. Thus, PD-L1 status was augmented-causative for nivolumab effectiveness, to an extent of 24.3%, and was neutral-activated. Conclusion Our approach characterizes the covariate effect types in terms of the response types, and to what extent. In a randomized clinical trial with a binary outcome, our approach is a potentially valuable addition to standard subgroup or regression analysis.