Numeric score-based conditional and overall change-in-status indices for ordered categorical data.
Numeric score-based conditional and overall change-in-status indices for ordered categorical data.
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有序分类数据的基于数字分数的条件和总体状态变化指数。
DOI：
10.1002/sim.6559
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发表时间：
2015
影响因子：
2
通讯作者：
Martin,SandraL
中科院分区：
文献类型：
--
作者：
Lyles,RobertH;Kupper,LawrenceL;Barnhart,HuimanX;Martin,SandraL
计划的干预和/或自然条件通常会影响有序分类结果的变化（例如，症状严重程度）。在这种情况下，有时需要将优先级分配给观察到的状态变化，通常为更大幅度的变化赋予更高的权重。我们根据c × c表中每行的多项式模型为此类数据定义变化指数，其中行表示基线状态类别。我们区分了一个指数，旨在评估每个基线类别内的条件变化，从其他两个旨在捕捉整体变化。其中一个总体指数衡量的是目标人群的预期变化。另一个是按比例缩放的，以捕捉数据所指示的方向上的总可能变化的比例，因此它的范围从-1（当所有受试者都在最不利的类别中完成时）到+1（当所有受试者都在最有利的类别中完成时）。无论如何将受试者抽样到基线类别中，变化的条件评估都可以提供信息。相比之下，当受试者在基线时从关注的目标人群中随机抽样时，或者当研究者能够对该人群的基线状态分布做出某些假设时，总体指数变得相关。我们使用Dirichlet-multinomial模型来获得条件变化指数的贝叶斯可信区间，这些区间表现出有利的小样本频率论属性。模拟研究说明了方法，我们将其应用到例子中，涉及睡眠剥夺和日常生活活动的研究顺序反应的变化。版权所有© 2015约翰威利父子有限公司.
Planned interventions and/or natural conditions often effect change on an ordinal categorical outcome (e.g., symptom severity). In such scenarios, it is sometimes desirable to assigna prioriscores to observed changes in status, typically giving higher weight to changes of greater magnitude. We define change indices for such data based upon a multinomial model for each row of a c × c table, where the rows represent the baseline status categories. We distinguish an index designed to assess conditional changes within each baseline category from two others designed to capture overall change. One of these overall indices measures expected change across a target population. The other is scaled to capture the proportion of total possible change in the direction indicated by the data, so that it ranges from −1 (when all subjects finish in the least favorable category) to +1 (when all finish in the most favorable category). The conditional assessment of change can be informative regardless of how subjects are sampled into the baseline categories. In contrast, the overall indices become relevant when subjects are randomly sampled at baseline from the target population of interest, or when the investigator is able to make certain assumptions about the baseline status distribution in that population. We use a Dirichlet‐multinomial model to obtain Bayesian credible intervals for the conditional change index that exhibit favorable small‐sample frequentist properties. Simulation studies illustrate the methods, and we apply them to examples involving changes in ordinal responses for studies of sleep deprivation and activities of daily living. Copyright © 2015 John Wiley & Sons, Ltd.