Sensitivity Analysis for Shared-Parameter Models for Incomplete Longitudinal Outcomes
Sensitivity Analysis for Shared-Parameter Models for Incomplete Longitudinal Outcomes
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DOI：
10.1002/bimj.200800235
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发表时间：
2010-02-01
影响因子：
1.7
通讯作者：
Kenward, Michael G.
中科院分区：
文献类型：
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作者：
Creemers, An;Hens, Niel;Kenward, Michael G.
所有不完整数据的模型要么明确地假设未观察到的结果的分布方面，给定观察到的结果，要么至少隐含地暗示这一点。一个结果是，通常存在一整类模型，它们对数据中观察到的部分的描述一致，但对未观察到的部分的“预测”不同。在这样一个类中，总是有一个单一的模型对应于所谓的随机缺失，在这个意义上，管理缺失的机制取决于协变量和观察到的结果，但给定这些不进一步对未观察到的结果。我们采用这些结果的背景下，所谓的共享参数模型的结果和缺失模型连接通过共同的潜变量或随机效应，设计一个敏感性分析框架。准确地说，不同的不可验证的假设未观察到的测量参数的影响进行了研究。除了分析的考虑，建议的方法，以评估治疗效果的数据，从临床试验中的趾甲皮肤真菌病。虽然我们的重点是纵向结果与不完整的结果数据，本文中开发的想法是有用的共享参数模型时，可以考虑。
All models for incomplete data either explicitly make assumptions about aspects of the distribution of the unobserved outcomes, given the observed ones, or at least implicitly imply such. One consequence is that there routinely exist a whole class of models, coinciding in their description of the observed portion of the data but differing with respect to their "predictions" of what is unobserved. Within such a class, there always is a single model corresponding to so-called random missingness, in the sense that the mechanism governing missingness depends on covariates and observed outcomes, but given these not further on unobserved outcomes. We employ these results in the context of so-called shared-parameter models where outcome and missingness models are connected by means of common latent variables or random effects, to devise a sensitivity analysis framework. Precisely, the impact of varying unverifiable assumptions about unobserved measurements on parameters of interest is studied. Apart from analytic considerations, the proposed methodology is applied to assess treatment effect in data from a clinical trial in toenail dermatophyte onychomycosis. While our focus is on longitudinal outcomes with incomplete outcome data, the ideas developed in this paper are of use whenever a shared-parameter model could be considered.