Cancer Diagnosis (DISCO) Group, College of Medicine and Health, St Luke's Campus, University of Exeter, Heavitree Road, Exeter, Devon, EX1 2LU, UK.
Department of Economics, Exeter Business School, University of Exeter, Rennes Drives, Exeter, Devon, EX4 4PU, UK.
Soc Sci Med. 2020 Aug;258:113021. doi: 10.1016/j.socscimed.2020.113021. Epub 2020 May 15.
We examine the suitability of three methods using patient-level data to evaluate the time-varying impacts of national healthcare guidelines. Such guidelines often codify progressive change and are implemented gradually; for example, National Institute for Health and Care Excellence (NICE) suspected-cancer referral guidelines. These were revised on June 23, 2015, to include more cancer symptoms and test results ("features"), partly reflecting changing practice. We explore the time-varying impact of guideline revision on time to colorectal cancer diagnosis, which is linked to improved outcomes in decision-analytic models. We included 11,842 patients diagnosed in 01/01/2006-31/12/2017 in the Clinical Practice Research Datalink with England cancer registry data linkage. Patients were classified by whether their first pre-diagnostic cancer feature was in the original guidelines (NICE-2005) or was added during the revision (NICE-2015-only). Outcome was diagnostic interval: time from first cancer feature to diagnosis. All analyses adjusted for age and sex. Two difference-in-differences analyses used either a Pre (01/08/2012-31/12/2014, n = 2243) and Post (01/08/2015-31/12/2017, n = 1017) design, or event-study cohorts (2006-2017 vs 2015) to estimate change in diagnostic interval attributable to official implementation of the revised guidelines. A semiparametric varying-coefficient model analysed the difference in diagnostic interval between the NICE groups over time. After model estimation, primary and broader treatment effects of guideline content and implementation were measured. The event-study difference-in-differences and the semiparametric varying-coefficient methods showed that shorter diagnostic intervals were attributable to official implementation of the revised guidelines. This impact was only detectable by pre-to-post difference-in-differences when the pre/post periods were selected according to the estimation results from the varying-coefficient model. Formal tests of the parametric models, which are special cases of the semiparametric model, suggest that they are misspecified. We conclude that the semiparametric method is well suited to explore the time-varying impacts of guidelines codifying progressive change.
我们研究了使用患者水平数据评估国家医疗保健指南随时间变化的影响的三种方法的适用性。此类指南通常规定了渐进式变化，并逐步实施；例如，英国国家卫生与保健优化研究所（NICE）疑似癌症转诊指南。该指南于 2015 年 6 月 23 日修订，纳入了更多癌症症状和检查结果（“特征”），部分反映了实践的变化。我们探讨了指南修订对结直肠癌诊断时间的时变影响，这与决策分析模型中改善的结果相关。我们纳入了 2006 年 1 月 1 日至 2017 年 12 月 31 日在临床实践研究数据链接中接受英格兰癌症登记处数据链接的 11842 例患者。患者分为其首次诊断前癌症特征是在原始指南（NICE-2005）中还是在修订期间添加（仅 NICE-2015）。结果是诊断间隔：从首次癌症特征到诊断的时间。所有分析均调整了年龄和性别。两个差异分析使用了 Pre（2012 年 8 月 1 日至 2014 年 12 月 31 日，n=2243）和 Post（2015 年 8 月 1 日至 2017 年 12 月 31 日，n=1017）设计，或事件研究队列（2006 年至 2017 年与 2015 年）来估计官方实施修订指南对诊断间隔变化的影响。半参数变系数模型分析了随时间推移 NICE 组之间诊断间隔的差异。在模型估计之后，测量了指南内容和实施的主要和更广泛的治疗效果。事件研究差异分析和半参数变系数方法表明，诊断间隔的缩短归因于修订指南的正式实施。仅在根据变系数模型的估计结果选择前后期间时，通过前后差异分析才能检测到这种影响。参数模型的正式检验表明，这些模型是不合适的，它们是半参数模型的特例。我们得出结论，半参数方法非常适合探索规定渐进式变化的指南的时变影响。
