Demirtas Hakan
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612-4336, USA.
Stat Med. 2005 Aug 15;24(15):2345-63. doi: 10.1002/sim.2117.
Conventional pattern-mixture models can be highly sensitive to model misspecification. In many longitudinal studies, where the nature of the drop-out and the form of the population model are unknown, interval estimates from any single pattern-mixture model may suffer from undercoverage, because uncertainty about model misspecification is not taken into account. In this article, a new class of Bayesian random coefficient pattern-mixture models is developed to address potentially non-ignorable drop-out. Instead of imposing hard equality constraints to overcome inherent inestimability problems in pattern-mixture models, we propose to smooth the polynomial coefficient estimates across patterns using a hierarchical Bayesian model that allows random variation across groups. Using real and simulated data, we show that multiple imputation under a three-level linear mixed-effects model which accommodates a random level due to drop-out groups can be an effective method to deal with non-ignorable drop-out by allowing model uncertainty to be incorporated into the imputation process.
传统的模式混合模型可能对模型误设高度敏感。在许多纵向研究中，由于失访的性质和总体模型的形式未知，任何单一模式混合模型的区间估计可能会出现覆盖不足的情况，因为未考虑模型误设的不确定性。在本文中，我们开发了一类新的贝叶斯随机系数模式混合模型来处理潜在的不可忽略的失访问题。我们不是通过施加严格的等式约束来克服模式混合模型中固有的不可估计性问题，而是建议使用层次贝叶斯模型对跨模式的多项式系数估计进行平滑处理，该模型允许组间存在随机变化。通过使用真实数据和模拟数据，我们表明，在一个三级线性混合效应模型下进行多次插补（该模型由于失访组而包含一个随机水平）可以成为一种有效的方法，通过将模型不确定性纳入插补过程来处理不可忽略的失访问题。
