首页> 外文期刊>The world journal of biological psychiatry: the official journal of the World Federation of Societies of Biological Psychiatry >The potential role of Marginal Structural Models (MSMs) in testing the effectiveness of antidepressants in the treatment of patients with major depression in everyday clinical practice
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The potential role of Marginal Structural Models (MSMs) in testing the effectiveness of antidepressants in the treatment of patients with major depression in everyday clinical practice

机译:在日常临床实践中,边缘结构模型(MSM)在测试抗抑郁药治疗重度抑郁症患者的有效性方面的潜在作用

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Objectives. To better evaluate the effectiveness of antidepressant drugs in the treatment of major depression in clinical practice. Methods. A simulation experiment was used to illustrate an application of marginal structural models (MSMs) via inverse probability of treatment weighting (IPTW) approach in the context of non-randomized data on N = 1,000 depressed subjects, initially subjected to "watchful waiting". In simulation we assumed that subjects with worse intermediate outcome have a higher probability of being subsequently assigned to antidepressant treatment while those who receive antidepressant treatment are more likely to reach remission and less likely to reach relapse state. The outcomes from multiple (500) simulated data sets are analyzed using simple unadjusted analysis based on logistic regression and using MSM. Results. In contrast to unadjusted analysis, but consistent with the treatment assumptions, using MSM via IPTW results in strong evidence of the effectiveness of the antidepressant treatment. Furthermore MSM via IPTW substantially reduces the probability of wrongly rejecting the null hypothesis. However, the instability of weights due to the sparse data and incorrectly specified MSM may still result in inflation of Type I error rates. Conclusions. MSMs may allow evaluating the causal effects associated with antidepressant treatment from the data observed in clinical practice.
机译:目标。为了在临床实践中更好地评估抗抑郁药在治疗重大抑郁症中的有效性。方法。模拟实验被用来说明在N = 1,000抑郁受试者的非随机数据的背景下,通过权重的逆概率(IPTW)方法应用边际结构模型(MSM),这些受试者最初受到“警惕的等待”。在模拟中,我们假设具有较差中间结果的受试者随后更有可能接受抗抑郁药治疗,而接受抗抑郁药治疗的患者更有可能达到缓解状态,而不太可能达到复发状态。使用基于逻辑回归的简单未经调整分析和MSM,可以分析来自多个(500)模拟数据集的结果。结果。与未经调整的分析相反,但与治疗假设一致,通过IPTW使用MSM可提供有力证据证明抗抑郁药治疗的有效性。此外,通过IPTW的MSM大大降低了错误拒绝无效假设的可能性。但是,由于数据稀疏和MSM指定不正确而导致的权重不稳定可能仍会导致I型错误率上升。结论。 MSMs可以根据临床实践中观察到的数据评估与抗抑郁药治疗相关的因果关系。

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