Validity and power of missing data imputation for extreme sampling and terminal measures designs in mediation analysis.
Validity and power of missing data imputation for extreme sampling and terminal measures designs in mediation analysis.
复制标题
DOI：
10.3389/fgene.2011.00075
复制
发表时间：
2011
影响因子：
3.7
通讯作者：
Allison DB
中科院分区：
文献类型：
--
作者：
Makowsky R;Beasley TM;Gadbury GL;Albert JM;Kennedy RE;Allison DB
几位作者承认，测试治疗、基因、生理测量和行为之间的中介假设可能会大大增进我们对这些关联如何运作的理解。在精神病学研究中，测量假定的中介因素或结果的成本可能令人望而却步。极端抽样设计已被证实是一种降低研究成本的方法，在评估二元关系时，可以通过增加每个受试者在更昂贵的变量上测量的功效来降低研究成本。然而，人们担心缺失的数据可能会导致结果出现偏差。此外，大多数中介分析技术都预设了对所有受试者的中介因素和结果的联合测量。在采用极端抽样的研究中，评估假定中介因素的技术方法学发展有限，导致数据缺失。我们证明，极端（选择性）抽样策略在中介分析中可能是有益的。以最大似然 (ML) 处理缺失数据可实现最小的功率损失和无偏参数估计。不过，在推荐极端采样设计的 ML 方法时我们必须谨慎，因为它在某些无效条件下会产生夸大的 1 类错误率。然而，使用极端抽样设计和方法来处理由此产生的缺失数据提供了一种可行的研究策略。
Several authors have acknowledged that testing mediational hypotheses between treatments, genes, physiological measures, and behaviors may substantially advance our understanding of how these associations operate. In psychiatric research, the costs of measuring the putative mediator or the outcome can be prohibitive. Extreme sampling designs have been validated as methods for reducing study costs by increasing power per subject measured on the more expensive variable when assessing bivariate relationships. However, there exist concerns about how missing data can potentially bias the results. Additionally, most mediation analysis techniques presuppose the joint measurement of mediators and outcomes for all subjects. There have been limited methodological developments for techniques that can evaluate putative mediators in studies that have employed extreme sampling, resulting in missing data. We demonstrate that extreme (selective) sampling strategies can be beneficial in the context of mediation analyses. Handling the missing data with maximum likelihood (ML) resulted in minimal power loss and unbiased parameter estimates. We must be cautious, though, in recommending the ML approach for extreme sampling designs because it yielded inflated Type 1 error rates under some null conditions. Yet, the use of extreme sampling designs and methods to handle the resultant missing data presents a viable research strategy.