Luijken Kim, van Eekelen Rik, Gardarsdottir Helga, Groenwold Rolf H H, van Geloven Nan
Department of Epidemiology, Utrecht University Medical Center, University Utrecht, Utrecht, The Netherlands.
Centre for Reproductive Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands.
Pharmacoepidemiol Drug Saf. 2023 Aug;32(8):863-872. doi: 10.1002/pds.5620. Epub 2023 Mar 29.
Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process.
We applied the ICH-E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands - which originally focused on randomized trials - to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population-level summary measure.
Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single-dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained-treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon-like peptide-1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as-needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris.
The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data-analysis and clarify how to interpret study findings.
理想情况下，药物流行病学比较有效性或安全性研究的目标应决定其设计和数据分析。本文讨论了定义估计量如何有助于这一过程。
我们将最初专注于随机试验的ICH-E9（临床试验统计原则）R1增编中关于估计量的内容应用于三项观察性药物流行病学比较有效性和安全性研究的实例。估计量由五个关键要素确定：人群、对比治疗、终点、并发事件和人群水平汇总指标。
针对代表三种药物治疗类型的案例研究定义了不同的估计量：（1）单剂量治疗，以一项关于≥60岁成年人群中流感疫苗接种与未接种疫苗对死亡风险影响的案例研究为例；（2）持续治疗，以一项关于二肽基肽酶4抑制剂与胰高血糖素样肽-1激动剂在治疗未控制糖尿病时对低血糖风险影响的案例研究为例；（3）按需治疗，以一项关于按需使用硝酸甘油喷雾剂与不使用硝酸甘油对稳定型心绞痛治疗中晕厥风险影响的案例研究为例。
案例研究表明，一个看似明确的研究问题仍可能有多种解释。定义估计量可确保研究针对与研究旨在为其提供信息的治疗决策相一致的治疗效果。估计量定义进一步有助于为研究设计和数据分析的选择提供信息，并阐明如何解释研究结果。
