...
首页> 外文期刊>The journal of clinical investigation >Concerns about the interpretation of subgroup analysis. Reply.
【24h】

Concerns about the interpretation of subgroup analysis. Reply.

机译:关于亚组分析解释的担忧。 回复。

获取原文
   

获取外文期刊封面封底 >>

       

摘要

The authors reply: We appreciate Albuquerque et al.’s interest in our paper ( 1 , 2 ), about which the authors of the Letter raised the concern that we did not accurately interpret the interaction test. Their Letter noted that “one should directly compare the estimates (interaction test)” and “the authors concluded that the association was only present in the African American population, which is not compatible with their analysis.” We would like to clarify that our primary clinical question was whether use of ACE inhibitors (ACE-Is) and angiotensin receptor blockers (ARBs) is associated with the COVID-19 outcomes in each subgroup. We used a stratified analysis to answer the question, because when race/ethnicity serves as a nonspecific proxy for numerous (confounding) factors, these can be (partially) controlled for through stratification ( 3 ). Joint modeling of multiple groups is often used to gain power, but one needs to assume certain coherent distributions across different groups, which is not always true. Additionally, testing the interaction term is to assess association heterogeneity between groups; it does not directly address whether the treatment is effective in each group. Specifically, we would like to elaborate on two points. First, our conclusion that the use of ARB was associated with a significant reduction in in-hospital mortality among African American patients but not non–African American patients was based on results from the stratified analysis. We reported that ARB in-hospital use was associated with reduced mortality in the African American stratum (OR = 0.196; 95% CI 0.074–0.516; P = 0.001) with statistical significance. On the other hand, the association in the non–African American stratum is not statistically significant (OR = 0.687; 95% CI 0.427–1.106; P = 0.122). As stated previously, our primary objective was to assess whether ACE-I/ARB use among African American patients is associated with COVID-19 mortality, rather than whether there is a difference between African American and non–African American patients. We were also aware that the estimated ORs across different stratum were not comparable as noted in ( 4 – 6 ). Second, we performed the joint modeling of African American and non–African American patients as suggested by Knol and VanderWeele ( 6 ). In our study, ARB in-hospital use was associated with reduced mortality in the entire study population (OR = 0.560; 95% CI 0.371–0.846; P = 0.006). The interaction term added to the model was not significant (95% CI 0.185–1.292; P = 0.149). Interpreting interaction terms in logistic regression is complex and a significant interaction term in log-odds may not be significant in difference-in-differences for probability ( 7 ). Furthermore, the assumption of the additive effects and imbalanced sample sizes could impact the inference. We believe these results and the interpretation are appropriate. We acknowledge that there are cases where comparing the interaction term in greater detail would be an important next step for understanding the association between COVID-19 mortality and race and ethnicity.
机译:作者回复:我们欣赏阿尔伯克基等人。对本文的兴趣(1,2),这封信的作者提出了我们没有准确解释互动测试的关切。他们的信指出,“一个人应该直接比较估计(互动测试)”和“作者得出的结论是,该协会仅在非洲裔美国人口中存在,这与他们的分析不兼容。”我们谨澄清我们的主要临床问题是使用ACE抑制剂(ACE-IS)和血管紧张素受体阻滞剂(ARBS)与每个亚组中的Covid-19结果相关。我们使用了分层分析来回答问题,因为当种族/民族作为无数(混淆)因素的非特异性代理时,这些可以(部分)通过分层(3)控制。多个组的联合建模通常用于获得功率,但是一个需要承担不同组的某些连贯的分布,这并不总是如此。另外,测试相互作用项是评估组之间的关联异质性;它没有直接解决治疗是否有效在每组中。具体而言,我们想详细说明两点。首先,我们的结论是,ARB的使用与非洲裔美国患者中的住院内死亡率显着降低相关,但非非洲裔美国患者基于来自分层分析的结果。我们报告说,ARB住院使用与非洲裔美国地层的死亡率降低(或= 0.196; 95%CI 0.074-0.516; P = 0.001),具有统计学意义。另一方面,非非洲裔美国地层的关联在统计学上没有统计学意义(或= 0.687; 95%CI 0.427-1.106; P = 0.122)。如前所述,我们的主要目标是评估非洲裔美国患者的ACE-I / ARB是否使用与Covid-19死亡率有关,而不是非洲裔美国和非洲裔美国人患者是否存在差异。我们也意识到不同层面的估计或者与(4-6)中所述的不同层的估计和数量不可比较。其次,我们通过Knol和Vanderweele建议的非洲裔美国和非非洲裔美国人的联合建模(6)。在我们的研究中,ARB在医院使用的使用与整个研究人群的死亡率降低有关(或= 0.560; 95%CI 0.371-0.846; P = 0.006)。添加到模型中的相互作用术语不显着(95%CI 0.185-1.292; P = 0.149)。解释逻辑回归中的交互术语是复杂的,并且在概率(7)的差异差异中,Log-odds中的显着交互术语可能不会显着。此外,添加剂效应和不平衡样本尺寸的假设可能会影响推理。我们相信这些结果,解释是合适的。我们承认有些情况下,更详细地比较互动术语将是理解Covid-19死亡率和种族和种族之间的关联的重要下一步。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号