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Robust estimation for longitudinal data under outcome‐dependent visit processes
Australian & New Zealand Journal of Statistics ( IF 1.1 ) Pub Date : 2020-07-04 , DOI: 10.1111/anzs.12290
John M. Neuhaus 1 , Charles E. McCulloch 1
Affiliation  

In longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when those assumptions are violated in order to guide practical use of the methods. This paper presents theory and the results of simulation studies to extend our previous work to more realistic visit process models, as well as Poisson outcomes. We also assess the effects of several types of model misspecification. The estimated bias in these new settings generally mirrors the theoretical and simulation results of our previous work and provides confidence in using maximum likelihood methods in practice. Even when the assumptions about the outcome process did not hold, mixed effects models fit by maximum likelihood produced at most small bias in estimated regression coefficients, illustrating the robustness of these methods. This contrasts with generalised estimating equations approaches where bias increased in the settings of this paper. The analysis of data from a study of change in neurological outcomes following microsurgery for a brain arteriovenous malformation further illustrate the results.

中文翻译:

在取决于结果的访问过程中对纵向数据进行可靠的估计

在纵向数据中,结果测量的时机和频率可能与结果的值相关联,可能会出现明显的偏差。先前的结果取决于对结果过程的正确说明以及某种程度不切实际的访问过程模型。实际上,情况永远不会完全如此,因此,重要的是要了解违反这些假设时结果在多大程度上可以指导方法的实际使用。本文介绍了仿真研究的理论和结果,以将我们以前的工作扩展到更现实的访问过程模型以及Poisson结果。我们还评估了几种类型的模型错误指定的影响。这些新设置中的估计偏差通常反映了我们先前工作的理论和模拟结果,并为在实践中使用最大似然法提供了信心。即使没有关于结果过程的假设,混合效应模型也可以通过估计回归系数中的最小偏差产生的最大似然拟合,从而说明了这些方法的鲁棒性。这与广义估计方程的方法形成对比,在本文的设置中偏差增加了。一项针对脑动静脉畸形的显微外科手术后神经系统结果变化研究的数据分析进一步说明了这一结果。混合效应模型通过估计回归系数中最大偏差产生的最大似然拟合,说明了这些方法的鲁棒性。这与广义估计方程的方法形成对比,在本文的设置中偏差增加了。一项针对脑动静脉畸形的显微外科手术后神经系统结果变化研究的数据分析进一步说明了这一结果。混合效应模型通过估计回归系数中最大偏差产生的最大似然拟合,说明了这些方法的鲁棒性。这与广义估计方程的方法形成对比,在本文的设置中偏差增加了。一项针对脑动静脉畸形的显微外科手术后神经系统结果变化研究的数据分析进一步说明了这一结果。
更新日期:2020-07-24
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