Fanshawe Thomas R, Turner Philip J, Gillespie Marjorie M, Hayward Gail N
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.
Practice Plus Group, Hawker House, 5-6 Napier Court, Napier Road, Reading, Berkshire, England, RG1 8BW, UK.
Diagn Progn Res. 2022 Mar 2;6(1):3. doi: 10.1186/s41512-022-00118-w.
In diagnostic evaluation, it is necessary to assess the clinical impact of a new diagnostic as well as its diagnostic accuracy. The comparative interrupted time series design has been proposed as a quasi-experimental approach to evaluating interventions. We show how it can be used in the design of a study to evaluate a point-of-care diagnostic test for C-reactive protein in out-of-hours primary care services, to guide antibiotic prescribing among patients presenting with possible respiratory tract infection. This study consisted of a retrospective phase that used routinely collected monthly antibiotic prescribing data from different study sites, and a prospective phase in which antibiotic prescribing rates were monitored after the C-reactive protein diagnostic was introduced at some of the sites.
Of 8 study sites, 3 were assigned to receive the diagnostic and 5 were assigned as controls. We obtained retrospective monthly time series of respiratory tract targeted antibiotic prescriptions at each site. Separate ARIMA models at each site were used these to forecast monthly prescription counts that would be expected in the prospective phase, using simulation to obtain a set of 1-year predictions alongside their standard errors. We show how these forecasts can be combined to test for a change in prescription rates after introduction of the diagnostic and estimate power to detect this change.
Fitted time series models at each site were stationary and showed second-order annual seasonality, with a clear December peak in prescriptions, although the timing and extent of the peak varied between sites and between years. Mean one-year predictions of antibiotic prescribing rates based on the retrospective time series analysis differed between sites assigned to receive the diagnostic and those assigned to control. Adjusting for the trend in the retrospective time series at each site removed these differences.
Quasi-experimental designs such as comparative interrupted time series can be used in diagnostic evaluation to estimate effect sizes before conducting a full randomised controlled trial or if a randomised trial is infeasible. In multi-site studies, existing retrospective data should be used to adjust for underlying differences between sites to make outcome data from different sites comparable, when possible.
在诊断评估中，有必要评估新诊断方法的临床影响及其诊断准确性。比较中断时间序列设计已被提议作为一种评估干预措施的准实验方法。我们展示了如何将其用于设计一项研究，以评估非工作时间基层医疗服务中C反应蛋白即时诊断检测，从而指导可能患有呼吸道感染患者的抗生素处方开具。本研究包括一个回顾性阶段，该阶段使用了来自不同研究地点的每月常规收集的抗生素处方数据，以及一个前瞻性阶段，在部分地点引入C反应蛋白诊断后监测抗生素处方率。
在8个研究地点中，3个被分配接受该诊断检测，5个被分配为对照组。我们获取了每个地点呼吸道靶向抗生素处方的回顾性月度时间序列。在每个地点使用单独的自回归积分滑动平均（ARIMA）模型，利用模拟获得一组1年预测值及其标准误差，以此预测前瞻性阶段预期的每月处方数量。我们展示了如何将这些预测结果结合起来，以检验引入该诊断检测后处方率的变化，并估计检测此变化的效能。
每个地点的拟合时间序列模型是平稳的，呈现二阶年度季节性，处方量在12月有明显峰值，尽管峰值的时间和幅度在不同地点和不同年份有所不同。基于回顾性时间序列分析的抗生素处方率的平均一年预测值在被分配接受该诊断检测的地点和被分配为对照组的地点之间存在差异。对每个地点回顾性时间序列中的趋势进行调整消除了这些差异。
诸如比较中断时间序列这样的准实验设计可用于诊断评估，以便在进行全面随机对照试验之前估计效应大小，或者在随机试验不可行时使用。在多地点研究中，应尽可能利用现有的回顾性数据来调整各地点之间的潜在差异，以使来自不同地点的结果数据具有可比性。
