Gianacas Christopher, Liu Bette, Kirk Martyn, Di Tanna Gian Luca, Belcher Josephine, Blogg Suzanne, Muscatello David J
School of Population Health, University of New South Wales, Sydney, Australia; The George Institute for Global Health, University of New South Wales, Sydney, Australia; NPS MedicineWise, Sydney, Australia.
School of Population Health, University of New South Wales, Sydney, Australia.
J Clin Epidemiol. 2023 Nov;163:102-110. doi: 10.1016/j.jclinepi.2023.10.003. Epub 2023 Oct 13.
Compare two approaches to analyzing time series data-interrupted time series with segmented regression (ITS-SR) and Bayesian structural time series using the CausalImpact R package (BSTS-CI)-highlighting advantages, disadvantages, and implementation considerations.
We analyzed electronic health records using each approach to estimate the antibiotic prescribing reduction associated with an educational program delivered to Australian primary care physicians between 2012 and 2017. Two outcomes were considered: antibiotics for upper respiratory tract infections (URTIs) and antibiotics of specified formulations.
For URTI indication prescribing, average monthly prescriptions changes were estimated at -4,550; (95% confidence interval, -5,486 to -3,614) and -4,270; (95% credible interval, -5,934 to -2,626) for ITS-SR and BSTS-CI, respectively. Similarly for specified formulation prescribing, monthly average changes were estimated at -7,923; (95% confidence interval, -15,887 to 40) for ITS-SR and -20,269; (95% credible interval, -25,011 to -15,635) for BSTS-CI.
Differing results between ITS-SR and BSTS-CI appear driven by divergent explanatory and outcome series trends. The BSTS-CI may be a suitable alternative to ITS-SR only if the explanatory series represent the secular trend of the outcome series before the intervention and are equally affected by exogenous or confounding factors. When appropriately applied, BSTS-CI provides an alternative to ITS with more readily interpretable Bayesian effect estimates.
比较两种分析时间序列数据的方法——使用分段回归的中断时间序列分析（ITS - SR）和使用因果影响R包的贝叶斯结构时间序列分析（BSTS - CI），突出其优点、缺点和实施要点。
我们采用每种方法分析电子健康记录，以估计2012年至2017年期间向澳大利亚初级保健医生提供的一项教育计划所带来的抗生素处方减少情况。考虑了两个结果：用于上呼吸道感染（URTI）的抗生素和特定剂型的抗生素。
对于URTI适应症处方，ITS - SR和BSTS - CI估计的每月平均处方变化分别为 - 4,550（95%置信区间， - 5,486至 - 3,614）和 - 4,270（95%可信区间， - 5,934至 - 2,626）。同样，对于特定剂型处方，ITS - SR估计的每月平均变化为 - 7,923（95%置信区间， - 15,887至40），BSTS - CI为 - 20,269（95%可信区间， - 25,011至 - 15,635）。
ITS - SR和BSTS - CI之间不同的结果似乎是由不同的解释性和结果序列趋势驱动的。只有当解释性序列代表干预前结果序列的长期趋势且同样受到外生或混杂因素影响时，BSTS - CI才可能是ITS - SR的合适替代方法。当适当应用时，BSTS - CI为ITS提供了一种替代方法，其贝叶斯效应估计更易于解释。
