Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout.
Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout.
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DOI：
10.1002/sim.3484
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发表时间：
2009-02-15
影响因子：
2
通讯作者：
Ebener, Patricla
中科院分区：
文献类型：
--
作者：
Paddock, Susan M.;Ebener, Patricla
关键词：
药物滥用治疗研究因不可忽视的缺失数据的普遍问题而变得复杂——即缺失数据的出现与未观察到的结果有关。由于客户提前离开治疗，经常会出现数据缺失的情况。在这种情况下，通常采用模式混合模型 (PMM) 对结果和缺失数据机制进行联合建模。 PMM 需要不可测试的假设来识别模型参数。因此，已经探索了几种参数识别方法，用于连续结果的纵向建模，并且在其他情况下开发了信息先验。在本文中，我们描述了对五位药物滥用治疗临床专家进行的专家访谈，他们熟悉药物滥用治疗的治疗社区模式以及使用变化维度工具收集的治疗过程评分。访谈的目的是获得关于在完成两次评估之前离开的客户的连续客户级治疗过程分数变化率的专家意见，并且数据未识别其治疗过程分数的变化率（斜率）。我们发现专家的意见与广泛使用的用于识别 PMM 参数的假设有很大不同。此外，主观事先评估允许人们正确解决识别 PMM 中的参数所需的主观决策中固有的不确定性，并衡量它们对分析得出的结论的影响。
Substance abuse treatment research is complicated by the pervasive problem of non-ignorable missing data – i.e., the occurrence of the missing data is related to the unobserved outcomes. Missing data frequently arise due to early client departure from treatment. Pattern-mixture models (PMMs) are often employed in such situations to jointly model the outcome and the missing data mechanism. PMMs require non-testable assumptions to identify model parameters. Several approaches to parameter identification have therefore been explored for longitudinal modeling of continuous outcomes, and informative priors have been developed in other contexts. In this paper, we describe an expert interview conducted with five substance abuse treatment clinical experts who have familiarity with the Therapeutic Community modality of substance abuse treatment and with treatment process scores collected using the Dimensions of Change Instrument. The goal of the interviews was to obtain expert opinion about the rate of change in continuous client-level treatment process scores for clients who leave before completing two assessments and whose rate of change (slope) in treatment process scores is unidentified by the data. We find that the experts’ opinions differed dramatically from widely-utilized assumptions used to identify parameters in the PMM. Further, subjective prior assessment allows one to properly address the uncertainty inherent in the subjective decisions required to identify parameters in the PMM and to measure their effect on conclusions drawn from the analysis.
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影响因子：
2.7
作者：
Leon, Andrew C.;Demirtas, Hakan;Hedeker, Donald
通讯作者：
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影响因子：
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