Zhang Fang, Wagner Anita K, Soumerai Stephen B, Ross-Degnan Dennis
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA.
J Clin Epidemiol. 2009 Feb;62(2):143-8. doi: 10.1016/j.jclinepi.2008.08.007. Epub 2008 Nov 17.
Interrupted time series (ITS) is a strong quasi-experimental research design, which is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals (CIs) around absolute and relative changes in outcomes calculated from segmented regression parameter estimates.
We used multivariate delta and bootstrapping methods (BMs) to construct CIs around relative changes in level and trend, and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for autocorrelated errors.
Using previously published time series data, we estimated CIs around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and the BM produced similar results.
BM is preferred for calculating CIs of relative changes in outcomes of time series studies, because it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.
中断时间序列（ITS）是一种强大的准实验研究设计，越来越多地用于评估卫生服务和政策干预措施的效果。我们描述并说明了两种围绕分段回归参数估计所计算出的结局绝对和相对变化来估计置信区间（CI）的方法。
我们使用多变量增量法和自抽样法（BM），基于对自相关误差进行校正的时间序列数据的分段线性回归分析，围绕水平和趋势的相对变化以及结局的绝对变化构建CI。
利用先前发表的时间序列数据，我们围绕与华法林相互作用的药物的处方警报对每月每10,000名华法林使用者的处方率的影响估计了CI。多变量增量法（MDM）和BM产生了相似的结果。
在计算时间序列研究结局的相对变化的CI时，BM更受青睐，因为当从模型中正确获得参数估计值时，它不需要大样本量。样本量较小时需要谨慎。
