Kurland Brenda F, Heagerty Patrick J
National Alzheimer's Coordinating Center, University of Washington, Department of Epidemiology, 4311 11th Ave NE #300, Seattle, WA 98105, USA.
Stat Med. 2004 Sep 15;23(17):2673-95. doi: 10.1002/sim.1850.
We extend the marginalized transition model of Heagerty to accommodate non-ignorable monotone drop-out. Using a selection model, weakly identified drop-out parameters are held constant and their effects evaluated through sensitivity analysis. For data missing at random (MAR), efficiency of inverse probability of censoring weighted generalized estimating equations (IPCW-GEE) is as low as 40 per cent compared to a likelihood-based marginalized transition model (MTM) with comparable modelling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and non-ignorable missing data, and both reduce bias noticeably by improving model fit.
我们扩展了希格蒂的边际化转移模型，以适应不可忽略的单调缺失。使用选择模型，弱识别的缺失参数保持不变，并通过敏感性分析评估其影响。对于随机缺失（MAR）数据，与具有可比建模负担的基于似然的边际化转移模型（MTM）相比，删失加权广义估计方程（IPCW-GEE）的效率低至40%。MTM和IPCW-GEE回归参数对于MAR和不可忽略的缺失数据均显示出模型误设偏差，并且两者都通过改善模型拟合显著降低了偏差。
