Cruz Maricela, Bender Miriam, Ombao Hernando
Department of Statistics, University of California, Irvine, CA, USA.
Sue & Bill Gross School of Nursing, University of California, Irvine, CA, USA.
Stat Med. 2017 Dec 20;36(29):4660-4676. doi: 10.1002/sim.7443. Epub 2017 Aug 29.
Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community.
当前的卫生政策要求更多地使用基于证据的护理服务，以改善患者的质量和安全结果。护理服务是复杂的，其组成部分相互作用且相互依存，这对传统的统计分析技术构成了挑战，尤其是在对可能因特定医疗护理方式的改变而“中断”的结果数据时间序列进行建模时。中断时间序列（ITS）是一种强大的准实验设计，能够推断考虑数据依赖性的干预措施的有效性。当前分析ITS数据的标准化方法没有对干预后的变异和相关性变化进行建模。这是一个关键限制，因为由于干预，数据变异性和依赖性发生变化是合理的。此外，现有的方法要么假设一个具有瞬时效应的预先指定的中断时间点，要么删除干预效果未完全实现的数据。在本文中，我们描述并开发了一种新颖的稳健中断时间序列（稳健-ITS）模型，该模型克服了这些遗漏和限制。稳健-ITS模型正式对以下方面进行推断：（1）识别变化点；（2）干预前和干预后相关性的差异；（3）干预前和干预后结果方差的差异；以及（4）干预前和干预后均值的差异。我们通过分析一家医院的患者满意度数据来说明所提出的方法，该医院实施并评估了一种新的护理服务模式作为感兴趣的干预措施。稳健-ITS模型在一个R Shiny工具箱中实现，该工具箱可供社区免费使用。
