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在一项随机对照试验中应用边际结构模型来解释次优依从性。

Application of the Marginal Structural Model to Account for Suboptimal Adherence in a Randomized Controlled Trial.

作者信息

Rochon James, Bhapkar Manjushri, Pieper Carl F, Kraus William E

机构信息

Rho Federal Systems, 6330 Quadrangle Drive, Chapel Hill, NC 27517, USA.

Duke Clinical Research Institute, 2400 Pratt Street, Durham, NC 27710, USA.

出版信息

Contemp Clin Trials Commun. 2016 Dec 15;4:222-228. doi: 10.1016/j.conctc.2016.10.005. Epub 2016 Nov 3.

Abstract

BACKGROUND

There is considerable interest in adjusting for suboptimal adherence in randomized controlled trials. A per-protocol analysis, for example removes individuals who fail to achieve a minimal level of adherence. One can also reassign non-adherers to the control group, censor them at the point of non-adherence, or cross them over to the control. However, there are biases inherent in each of these methods. Here, we describe an application of causal modeling to address this issue.

METHODS

The marginal structural model with inverse-probability weighting was implemented using a weighted generalized estimating equation model. Two ancillary models were developed to derive the weights. First, stepwise linear regression was used to model the observed percent weight loss, while stepwise logistic regression model was applied to model early discontinuation from the intervention. From these, participant- and time-specific weights were calculated.

DISCUSSION

This model is complicated and requires careful attention to detail. Which variables to force into the ancillary models, how to construct interaction terms, and how to address time-dependent covariates must be considered. Nevertheless, it can be used to great effect to predict intervention effects at full adherence. Moreover, by contrasting these results against intention-to-treat results, insights can be gained into the intrinsic physiologic effect of the intervention.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT00427193.

摘要

背景

在随机对照试验中,针对不完全依从性进行调整受到了广泛关注。例如,按方案分析会排除那些未达到最低依从水平的个体。也可以将不依从者重新分配到对照组,在不依从点对他们进行截尾,或者将他们转为对照组。然而,这些方法都存在内在偏差。在此,我们描述一种因果建模的应用来解决这个问题。

方法

使用加权广义估计方程模型实施带逆概率加权的边际结构模型。开发了两个辅助模型来推导权重。首先,使用逐步线性回归对观察到的体重减轻百分比进行建模,同时应用逐步逻辑回归模型对干预早期停药进行建模。由此计算出参与者特定和时间特定的权重。

讨论

该模型很复杂,需要仔细关注细节。必须考虑将哪些变量强制纳入辅助模型、如何构建交互项以及如何处理随时间变化的协变量。尽管如此,它可用于有效预测完全依从时的干预效果。此外,通过将这些结果与意向性分析结果进行对比,可以深入了解干预的内在生理效应。

试验注册

ClinicalTrials.gov标识符NCT00427193。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ee8/5935891/236fd370450f/gr1.jpg

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