Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models
Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models
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DOI：
10.1037/a0035628
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发表时间：
2014-10-01
影响因子：
5.9
通讯作者：
Atkins, David C.
中科院分区：
文献类型：
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作者：
Baldwin, Scott A.;Imel, Zac E.;Atkins, David C.
目的：多水平模型已成为干预研究中的标准数据分析方法。虽然绝大多数的干预研究涉及多个结果的措施，很少有研究使用多变量分析方法。作者讨论了多变量扩展的多层次模型，可用于心理治疗研究人员。方法和结果：使用模拟纵向治疗数据，作者展示了多变量模型如何扩展常见的单变量增长模型，以及多变量模型如何用于检查涉及固定效应的多变量假设（例如，治疗效果的大小是否因结果而异？）以及随机效应（例如，一个结果的变化是否与另一个结果的变化有关？）。在线补充附录提供了注释的计算机代码和模拟的例子数据，用于实现多变量模型。结论：多变量多水平模型是一种灵活、功能强大的模型，可以加强临床研究。
Objective: Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results: Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions: Multivariate multilevel models are flexible, powerful models that can enhance clinical research.