Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure
Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure
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DOI：
10.1191/0962280205sm407oa
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发表时间：
2005-08-01
影响因子：
2.3
通讯作者：
Vansteelandt, S
中科院分区：
文献类型：
--
作者：
Goetghebeur, E;Vansteelandt, S
部分遵守指定的治疗方案在药物试验中很常见，需要对实际接受的治疗效果进行因果分析。由于这种观察到的暴露不再是随机的，因此必须仔细考虑选择偏倚。潜在结果的框架通过定义特定于受试者的无治疗参考结果来实现这一点，该参考结果可能是潜在的，并根据观察到的(治疗的)数据进行建模。因果参数明确地进入了这些结构模型。本文综述了结构均值模型的随机化推理的最新进展，从加性线性模型到结构广义线性模型。目前可用于标准关联回归的工具库已在结构设置中稳步发展，提供了许多并行特征来帮助基于随机化的推理。我们认为，曝光时的测量误差是一个重要的实际复杂问题，但尚未得到解决。我们展示了标准的加性线性结构平均模型如何对无偏测量误差具有健壮性，以及当测量误差偏差程度已知时如何得出有效的渐近无偏推断。以血压为例说明了测量误差的影响，并通过仿真验证了有限样本的性质。最后，我们呼吁更多、更谨慎地使用这一方法，并指出进一步发展的方向。
Partial compliance with assigned treatment regimes is common in drug trials and calls for a causal analysis of the effect of treatment actually received. As such observed exposure is no longer randomized, selection bias must be carefully accounted for. The framework of potential outcomes allows this by defining a subject-specific treatment-free reference outcome, which may be latent and is modelled in relation to the observed ( treated) data. Causal parameters enter these structural models explicitly. In this paper we review recent progress in randomization-based inference for structural mean modelling, from the additive linear model to the structural generalized linear models. An arsenal of tools currently available for standard association regression has steadily been developed in the structural setting, providing many parallel features to help randomization-based inference. We argue that measurement error on exposure is an important practical complication that has, however, not yet been addressed. We show how standard additive linear structural mean models are robust against unbiased measurement error and how efficient, asymptotically unbiased inference can be drawn when the degree of measurement error bias is known. The impact of measurement error is illustrated in a blood pressure example and finite sample properties are verified by simulation. We end with a plea for more and careful use of this methodology and point to directions for further development.