Demirtas Hakan, Schafer Joseph L
Department of Statistics and The Methodology Center, Pennsylvania State University, University Park, PA 16802, U.S.A.
Stat Med. 2003 Aug 30;22(16):2553-75. doi: 10.1002/sim.1475.
Random-coefficient pattern-mixture models (RCPMMs) have been proposed for longitudinal data when drop-out is thought to be non-ignorable. An RCPMM is a random-effects model with summaries of drop-out time included among the regressors. The basis of every RCPMM is extrapolation. We review RCPMMs, describe various extrapolation strategies, and show how analyses may be simplified through multiple imputation. Using simulated and real data, we show that alternative RCPMMs that fit equally well may lead to very different estimates for parameters of interest. We also show that minor model misspecification can introduce biases that are quite large relative to standard errors, even in fairly small samples. For many scientific applications, where the form of the population model and nature of the drop-out are unknown, interval estimates from any single RCPMM may suffer from undercoverage because uncertainty about model specification is not taken into account.
当认为失访不可忽略时，已针对纵向数据提出了随机系数模式混合模型（RCPMMs）。RCPMM是一种随机效应模型，其中失访时间的汇总包含在回归变量中。每个RCPMM的基础都是外推法。我们回顾了RCPMMs，描述了各种外推策略，并展示了如何通过多重填补简化分析。使用模拟数据和实际数据，我们表明拟合效果相同的替代RCPMMs可能会导致对感兴趣参数的估计非常不同。我们还表明，即使在相当小的样本中，轻微的模型错误设定也可能引入相对于标准误差而言相当大的偏差。对于许多科学应用，在总体模型的形式和失访的性质未知的情况下，任何单个RCPMM的区间估计可能会因未考虑模型设定的不确定性而出现覆盖不足的问题。
