Response to “Importance of method assumptions”

IF 4.2 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Obesity Pub Date : 2024-05-21 DOI:10.1002/oby.24056
Kaitlin H. Wade, Nicholas J. Timpson, Fergus W. Hamilton, Naveed Sattar, David Carslake, George Davey Smith
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Abstract

TO THE EDITOR: We read with interest the recent letter concerning our self-criticism [(1)] of the nonlinear Mendelian randomization (MR) methodology that we applied in an earlier publication in this journal [(2)]. In a constructive critique, the authors challenge the suitability of assigned sex as a negative control outcome under the definition that a valid negative control outcome should not be plausibly related to the exposure (here, body mass index [BMI]) and that these conditions can be tested within an analysis framework free of confounding and selection bias.

This is a reasonable point of consideration and one that justifiably cross-examines our use of the term “negative control.” However, this flags a more general issue—that our use of this term may not have been helpful from a formal point of view. Sadly, we fear that, again, the likely complications of nonlinear MR as described in our recent Perspective have been missed—that there are fundamental difficulties in the application of nonlinear MR analyses and that current methods (including the doubly ranked approach) are unable to avoid the generation of spurious associations.

We concede that the use of the UK Biobank as a population sample is potentially imperfect (mostly due to the biased, volunteer-based sampling frame) [(3)] for the deployment of a pure negative control analysis, as it will be for many other forms of both observational and applied epidemiological investigations. We have not provided an exhaustive simulation of the likely sources of possible bias to this extent, nor have we undertaken the same analysis in multiple independent studies (of different design) in efforts to quantify such flaws. However, what is important is that the analyses of both primary outcome and negative control (here, assigned sex) were undertaken in the same study and will suffer many of the same problems while remaining implausibly related; BMI may well be related to assigned sex, but BMI does not cause the latter. Importantly, the magnitude of selection bias within this sample and the specific question is unlikely to be large enough to explain these results, and, even if BMI was introducing the selection bias, this would influence the overall MR results as much as was seen using nonlinear methods and most likely not in a different and incomprehensible manner [(4, 5)].

There are a series of other potential arguments that would have been more well placed targeting our own use of the phrase negative control. For example, we may well expect a variable claimed to be an “instrument” for BMI that is derived from a polygenic risk score [(4)] to behave differently across strata of BMI (however formed), owing to the dimorphic characteristics of the underpinning genome-wide association study results. This is a potential biological flaw to the analysis and indeed one in which the differences in stratum-specific estimates would be induced by the nature of relationship between instrument and negative control. However, even this, with anticipated properties, would not generate the clearly flawed results generated in the illustrative analyses. Critically, the undertaking of nonlinear MR (even with new methods available that continue to generate questionable results) [(1, 6)] remains unreliable and is likely influenced by a host of features manifest with differing impact across studies (e.g., genetic architecture of exposures, outcome of interest, discovery genetic studies, distribution of exposure and relationship between exposure and outcome).

We welcome the authors' overall point regarding the need for the use of triangulation to ameliorate, or at least contextualize, inferential difficulties encountered in any study design. However, we are still unconvinced that the refinements to analyses or wording are such to correct the problems flagged in our exemplar analysis from 2018 or to rescue existing methods attempting to undertake nonlinear MR.

The authors declared no conflict of interest.

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致编辑:我们饶有兴趣地阅读了最近一封关于我们对本刊早前发表的一篇文章[(2)]中采用的非线性孟德尔随机(MR)方法进行自我批评的来信[(1)]。在建设性的批评意见中,作者对指定性别作为阴性对照结果的适宜性提出了质疑,其定义是:有效的阴性对照结果不应该与暴露(此处为体重指数[BMI])有似是而非的关系,而且这些条件可以在没有混杂和选择偏差的分析框架内进行测试。然而,这也标志着一个更为普遍的问题--从形式上看,我们使用这一术语可能并无益处。遗憾的是,我们担心,我们在最近的《视角》中描述的非线性磁共振可能出现的并发症再次被忽视了--非线性磁共振分析的应用存在根本性的困难,目前的方法(包括双重排序法)无法避免产生虚假关联。我们承认,使用英国生物库作为人群样本可能并不完美(主要是由于以志愿者为基础的抽样框架存在偏差)[(3)],无法进行纯阴性对照分析,正如许多其他形式的观察性流行病学调查和应用流行病学调查一样。我们并没有在这种程度上对可能的偏倚来源进行详尽的模拟,也没有在多个独立研究(不同的设计)中进行相同的分析,以量化这些缺陷。然而,重要的是,对主要结果和负面对照(此处为指定性别)的分析是在同一项研究中进行的,因此会出现许多相同的问题,同时仍然存在难以置信的关联;BMI 很可能与指定性别有关,但 BMI 并不导致指定性别。重要的是,该样本和具体问题中的选择偏差程度不太可能大到足以解释这些结果,而且,即使 BMI 带来了选择偏差,这也会像使用非线性方法一样影响 MR 的总体结果,而且很可能不会以不同的、难以理解的方式影响 MR 的总体结果[(4,5)]。例如,由于全基因组关联研究结果的二态性特征,我们完全可以预料到一个声称是体重指数 "工具 "的变量,在不同的体重指数分层(无论如何形成)中会有不同的表现[(4)]。这是分析的一个潜在生物学缺陷,实际上,工具和负对照之间的关系性质会导致特定分层估计值的差异。然而,即使是这种具有预期特性的情况,也不会产生说明性分析中产生的明显有缺陷的结果。至关重要的是,非线性 MR(即使有新的方法,但仍会产生值得怀疑的结果)[(1, 6)]的应用仍然不可靠,而且很可能受到一系列特征的影响,这些特征在不同的研究中会产生不同的影响(例如,暴露的遗传结构、感兴趣的结果、发现性遗传研究、暴露的分布以及暴露与结果之间的关系)。我们欢迎作者提出的总体观点,即需要使用三角测量来改善或至少说明在任何研究设计中遇到的推论困难。然而,我们仍然不相信,对分析或措辞的改进能够纠正我们在 2018 年的示例分析中指出的问题,或挽救试图进行非线性 MR 的现有方法。
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来源期刊
Obesity
Obesity 医学-内分泌学与代谢
CiteScore
11.70
自引率
1.40%
发文量
261
审稿时长
2-4 weeks
期刊介绍: Obesity is the official journal of The Obesity Society and is the premier source of information for increasing knowledge, fostering translational research from basic to population science, and promoting better treatment for people with obesity. Obesity publishes important peer-reviewed research and cutting-edge reviews, commentaries, and public health and medical developments.
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