Clinical Research Center, Kinki University Hospital, Osakasayama, Japan.
Clin Trials. 2019 Jun;16(3):237-245. doi: 10.1177/1740774519828301. Epub 2019 Feb 13.
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.
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.
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.
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.
背景/目的：一些随机临床试验旨在确定协变量效应类型，以确定协变量是否具有预测性和/或预后性，除此之外还评估终点。在这里，对于二项结局的情况，我们建议根据四种潜在反应类型来评估协变量效应类型：激活型（始终）、惰性型（从不）、因果型和预防型反应者。
我们引入了一个新的协变量效应类型概念，与常用的“预测”和“预后”不同。我们通过检查协变量两个亚组中每种反应类型的受试者比例来总结协变量效应类型，并指出当协变量水平改变时，分数是增加、减少还是中性。虽然这些比例通常无法确定，但我们可以通过应用最近开发的贝叶斯方法来推导出这些比例的后验分布。在此基础上，如果两个亚组中因果反应者（如果接受治疗，他们会有反应，但如果不接受治疗，他们不会有反应）的比例差异为正，则可以说协变量是“增强因果型”，而不是说它具有预测性。同样，如果激活反应者（无论随机治疗分配如何，他们都会有反应）的比例差异接近零，则可以说协变量是“中性激活型”，而不是说它没有预后性。我们进一步描述了我们的方法与标准亚组分析之间的关系。
我们将我们的方法应用于一项比较纳武单抗和多西他赛用于晚期非鳞状非小细胞肺癌患者的随机临床试验的数据；我们评估了 PD-L1 状态的协变量效应类型，PD-L1 是表达于激活 T 细胞上的程序性死亡 1（PD-1）受体的配体。当终点是总缓解率时，在 PD-L1 阳性和阴性亚组中反应类型比例之间差异的后验分布得出因果反应者的预期后验估计值为 0.243（95%可信区间（CI）：0.094，0.374），激活反应者为 0.014（95% CI：-0.087，0.125）。因此，对于纳武单抗的疗效，PD-L1 状态是增强因果型，增强程度为 24.3%，是中性激活型。
我们的方法根据反应类型及其程度来描述协变量效应类型。在具有二项结局的随机临床试验中，我们的方法是标准亚组或回归分析的一种潜在有价值的补充。
