A generalized interrupted time series model for assessing complex health care interventions.
A generalized interrupted time series model for assessing complex health care interventions.
复制标题
用于评估复杂的医疗保健干预措施的广义中断时间序列模型。
DOI：
10.1007/s12561-022-09346-6
复制
发表时间：
2022
影响因子：
1
通讯作者：
Gillen,DanielL
中科院分区：
文献类型：
--
作者：
Cruz,Maricela;Ombao,Hernando;Gillen,DanielL
评估复杂干预措施对可衡量的健康结果的影响是卫生保健和卫生政策中日益关注的问题。中断时间序列(ITS)设计借鉴了传统的病例交叉设计，具有准实验方法的功能，能够回顾分析干预的影响。用于分析其设计的统计模型主要关注连续价值的结果。我们提出了适用于基本分布属于指数分布族的结果的广义稳健ITS(GRITS)模型，从而扩展了现有的方法来适当地模拟二元和计数响应。GRITS正式实现了对离散ITS中变化点的存在的测试。所提出的方法能够测试和估计变化点的存在，能够在多个单元的设置中跨单元借用信息，并测试干预前后平均函数和相关性的差异。该方法以一家在多个单元实施和评估新的护理提供模式的医院的患者跌倒为例进行了说明。
Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the “Generalized Robust ITS" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units.