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首页> 外文期刊>Clinical trials: journal of the Society for Clinical Trials >Estimating causal effects using prior information on nontrial treatments.
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Estimating causal effects using prior information on nontrial treatments.

机译:使用有关非审判治疗的先验信息估算因果关系。

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摘要

BACKGROUND: Departures from randomized treatments complicate the analysis of many randomized controlled trials. Intention-to-treat analysis estimates the effect of being allocated to treatment. It is now possible to estimate the effect of receiving treatment without assuming comparability of groups defined by actual treatment. However, the methodology is largely confined to trials where the only treatment changes were switches to other trial treatments. PURPOSE: To propose a method for comparing the effects of receiving trial treatments in an active-controlled clinical trial where some participants received nontrial treatments and others received no treatment at all, and to illustrate the method in the PENTA 5 trial in HIV-infected children. METHODS: We combine the instrumental variables approach, which forms unbiased estimating equations based on the randomization but does not fully identify the contrasts of trial treatment effects, with prior information about the distribution of possible effects of nontrial treatments and of one trial treatment; we do not need to use prior information about the comparisons of trial treatments. Prior information in PENTA 5 was elicited from the investigators. RESULTS: In PENTA 5, the prior information suggested that all treatments were beneficial, but there was uncertainty about the degree of benefit. Allowing for this prior information changed point estimates and increased standard errors compared with ignoring nontrial treatments. LIMITATIONS: The method depends on the correct specification of the causal effect of treatment: in PENTA 5, this assumed a linear effect of dose and no interactions between treatments. This specification is hard to check from the data but can be explored in sensitivity analyses. Prior information would be better derived from the literature whenever possible. CONCLUSIONS: The use of partial prior information gives a way to adjust for complex patterns of departures from randomized treatments. It should be useful in all trials where nontrial treatments are used and in active-controlled trials where trial treatments are not universally used.
机译:背景:随机治疗的偏离使许多随机对照试验的分析变得复杂。意向治疗分析估计了分配给治疗的效果。现在可以估计接受治疗的效果,而无需假设实际治疗定义的组具有可比性。但是,该方法学主要限于试验,其中唯一的治疗更改是改用其他试验治疗。目的:提出一种在主动控制的临床试验中比较接受试验治疗的效果的方法,其中一些参与者接受了非试验治疗,而其他参与者则根本没有接受治疗,并说明了在PENTA 5试验中对HIV感染儿童的方法。方法:我们结合了工具变量方法,该方法基于随机形成无偏估计方程,但不能完全识别试验治疗效果的差异,以及有关非试验治疗和一种试验治疗可能影响的分布的先验信息;我们不需要使用有关比较试验治疗的先验信息。研究人员从PENTA 5中获得了先前的信息。结果:在PENTA 5中,先前的信息表明,所有治疗方法均有益,但获益程度尚不确定。与忽略非审判治疗相比,考虑到这种先验信息,可以改变点估计值并增加标准误。局限性:该方法取决于正确的治疗因果关系规范:在PENTA 5中,假定剂量呈线性效应,且治疗之间无相互作用。很难从数据中检查该规格,但可以在灵敏度分析中进行探索。只要有可能,最好从文献中获取先验信息。结论:部分先验信息的使用为适应随机治疗偏离的复杂模式提供了一种方法。在使用非试验治疗的所有试验中以及在未普遍使用试验治疗的主动对照试验中,它都应有用。

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