Improving point predictions of random effects for subjects at high risk
Improving point predictions of random effects for subjects at high risk
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DOI：
10.1002/sim.2614
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发表时间：
2007-03-15
影响因子：
2
通讯作者：
Cook, Curtiss B.
中科院分区：
文献类型：
--
作者：
Lyles, Robert H.;Manatunga, Amita K.;Cook, Curtiss B.
在医学和流行病学研究中，预测与受试者特定特征（例如变化的平均值或变化率）相对应的随机效应非常有用。有时，人们可能最感兴趣的是获得针对其特征将其置于分布尾部的受试者的准确和/或精确预测。虽然典型的后验均值预测在总体预测均方误差（MSEP）方面占主导地位，但其“过度收缩”的趋势促使人们研究强调其他标准的替代品。在这里，我们专门针对特定区域内的MSEP（例如，高于高风险的已知截止值或随机效应分布的指定百分位数），我们考虑在对总体MSEP效率有约束和无约束的情况下最小化该数量。我们使用正态理论随机截距模型来推导预测方法，这些方法有可能为指定区域的受试者提供明显更好的性能，给出了良好控制的总体MSEP标准（如果需要）适度让步，这些标准适用于分类以及整体和区域预测无偏性。我们评估所提出的技术，并说明他们使用重复测量数据的空腹血糖2型糖尿病患者。仿真研究验证了所提出的预测的理论性质和相对性能基本上保持计算时，他们在实践中估计的混合线性模型参数的基础上。简要概述了合并协变量和其他随机效应的简单扩展。版权所有（c）2006约翰威利父子有限公司。
The prediction of random effects corresponding to subject-specific characteristics (e.g. means or rates of change) can be very useful in medical and epidemiologic research. At times, one may be most interested in obtaining accurate and/or precise predictions for subjects whose characteristic places them in a tail of the distribution. While the typical posterior mean predictor dominates others in terms of overall mean squared error of prediction (MSEP), its tendency to 'overshrink' has motivated research into alternatives emphasizing other criteria. Here, we specifically target MSEP within a certain region (e.g. above a known cut-off for high risk or a specified percentile of the random effect distribution), and we consider minimizing this quantity with and without constraints on overall MSEP efficiency. We use the normal-theory random intercept model to derive prediction methods with potential to yield markedly better performance for subjects in the specified region, given a well-controlled and (if desired) modest concession of overall MSEP Criteria geared toward classification as well as overall and regional prediction unbiasedness are also provided. We evaluate the proposed techniques and illustrate them using repeated measures data on fasting blood glucose from type 2 diabetes patients. A simulation study verifies that theoretical properties and relative performances of the proposed predictors are essentially maintained when calculating them in practice based on estimated mixed linear model parameters. Straightforward extensions to incorporate covariates and additional random effects are briefly outlined. Copyright (c) 2006 John Wiley & Sons, Ltd.