通过中断时间序列模型评估卫生保健干预措施：研究功效和设计考虑
Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations
摘要：
优质医疗服务的提供和评估是复杂的，有许多相互作用和相互依赖的组成部分。在研究设计和统计分析方面，这种复杂性和相互依赖性使得难以评估旨在改善患者医疗保健结果的干预措施的真正影响。中断时间序列（ITS）是一种准试验设计，用于推断卫生政策干预的有效性，同时考虑单个系统或单元内的时间依赖性。目前的标准化ITS方法不能同时分析几个单位的数据，也没有方法来测试是否存在变化点，并评估统计功效，用于研究计划的目的。为了解决这一局限性，我们提出了“鲁棒多ITS”（R-MITS）模型，适用于多单元ITS数据，允许在存在潜在滞后（或预期）处理效应的情况下，对跨单元的全局变化点的估计进行推断。在R-MITS模型下，人们可以正式测试是否存在变化点，并估计正式干预实施与整体干预效果之间的时间延迟。我们进行了实证模拟研究，以评估第一类错误率的测试程序，电源检测指定的变点的替代品，和建议的估计方法的准确性。R-MITS是通过分析患者满意度数据，从医院实施和评估一个新的医疗服务模式，在多个单位。
英文摘要：
The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units, nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation we propose the `Robust Multiple ITS' (R-MITS) model, appropriate for multi-unit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.
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