Bias reduction in effectiveness analyses of longitudinal ordinal doses with a mixed-effects propensity adjustment
Bias reduction in effectiveness analyses of longitudinal ordinal doses with a mixed-effects propensity adjustment
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DOI：
10.1002/sim.2458
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发表时间：
2007-01-15
影响因子：
2
通讯作者：
Teres, Jedediah J.
中科院分区：
文献类型：
--
作者：
Leon, Andrew C.;Hedeker, Donald;Teres, Jedediah J.
混合效应的倾向调整，可以减少在纵向研究中的偏倚，涉及非等效的比较组。这种数据分析策略有两个阶段。首先，治疗强度的倾向模型使用混合效应有序逻辑回归来检查在不同时间接受不同顺序剂量治疗的受试者之间进行区分的变量。其次，有效性模型检查多次，直到复发，以比较使用混合效应分组时间生存模型的有序剂量。有效性分析最初按倾向五分位数分层。然后汇总五分位数特异性结果，假设不存在倾向x治疗相互作用。蒙特卡罗模拟研究比较了完全指定倾向模型相对于错误指定模型的偏倚减少。此外，I型错误率和统计功率进行了检查。该方法说明了它的纵向，观察性研究的维持治疗的抑郁症。版权所有（c）2005年约翰威利父子有限公司。
A mixed-effects propensity adjustment is described that can reduce bias in longitudinal studies involving non-equivalent comparison groups. There are two stages in this data analytic strategy. First, a model of propensity for treatment intensity examines variables that distinguish among subjects who receive various ordered doses of treatment across time using mixed-effects ordinal logistic regression. Second, the effectiveness model examines multiple times until recurrence to compare the ordered doses using a mixed-effects grouped-time survival model. Effectiveness analyses are initially stratified by propensity quintile. Then the quintile-specific results are pooled, assuming that there is not a propensity x treatment interaction. A Monte Carlo simulation study compares bias reduction in fully specified propensity model relative to misspecified models. In addition, type I error rate and statistical power are examined. The approach is illustrated by applying it to a longitudinal, observational study of maintenance treatment of major depression. Copyright (c) 2005 John Wiley & Sons, Ltd.