Applying the Estimands Framework to Non-Inferiority Trials: Guidance on Choice of Hypothetical Estimands for Non-Adherence and Comparison of Estimation Methods.

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Katy E Morgan, Ian R White, Clémence Leyrat, Simon Stanworth, Brennan C Kahan
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引用次数: 0

Abstract

A common concern in non-inferiority (NI) trials is that non-adherence due, for example, to poor study conduct can make treatment arms artificially similar. Because intention-to-treat analyses can be anti-conservative in this situation, per-protocol analyses are sometimes recommended. However, such advice does not consider the estimands framework, nor the risk of bias from per-protocol analyses. We therefore sought to update the above guidance using the estimands framework, and compare estimators to improve on the performance of per-protocol analyses. We argue the main threat to validity of NI trials is the occurrence of "trial-specific" intercurrent events (IEs), that is, IEs which occur in a trial setting, but would not occur in practice. To guard against erroneous conclusions of non-inferiority, we suggest an estimand using a hypothetical strategy for trial-specific IEs should be employed, with handling of other non-trial-specific IEs chosen based on clinical considerations. We provide an overview of estimators that could be used to estimate a hypothetical estimand, including inverse probability weighting (IPW), and two instrumental variable approaches (one using an informative Bayesian prior on the effect of standard treatment, and one using a treatment-by-covariate interaction as an instrument). We compare them, using simulation in the setting of all-or-nothing compliance in two active treatment arms, and conclude both IPW and the instrumental variable method using a Bayesian prior are potentially useful approaches, with the choice between them depending on which assumptions are most plausible for a given trial.

将估计框架应用于非劣效性试验:非依从性假设估计的选择指南和估计方法的比较。
在非劣效性(NI)试验中,一个常见的问题是,例如,由于不良的研究行为而导致的非依从性可能使治疗组人为地相似。因为意向治疗分析在这种情况下可能是反保守的,所以有时建议采用按协议分析。然而,这样的建议没有考虑估计框架,也没有考虑每个协议分析的偏差风险。因此,我们试图使用估算框架更新上述指南,并比较估算器以改进每个协议分析的性能。我们认为,对NI试验有效性的主要威胁是“试验特异性”交互事件(IEs)的发生,即在试验环境中发生的事件,但在实践中不会发生。为了防止非劣效性的错误结论,我们建议对试验特异性IEs采用假设策略进行估计,并根据临床考虑选择处理其他非试验特异性IEs。我们概述了可用于估计假设估计的估计器,包括逆概率加权(IPW)和两种工具变量方法(一种使用标准治疗效果的信息贝叶斯先验,另一种使用协变量治疗相互作用作为工具)。我们对它们进行了比较,在两个积极治疗组的全或无依从性设置中使用模拟,并得出结论,IPW和使用贝叶斯先验的工具变量方法都是潜在有用的方法,它们之间的选择取决于哪种假设对给定试验最合理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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