{"title":"外部比较器和估算器","authors":"Gerd Rippin","doi":"10.3389/fdsfr.2023.1332040","DOIUrl":null,"url":null,"abstract":"The estimand framework as defined by the ICH E9(R1) addendum aims to clearly define “the treatment effect reflecting the clinical question posed by the trial objective”. It intends to achieve this goal of a clear definition by specifying the 5 estimand attributes: treatment conditions, population, endpoints, handling of intercurrent events (IEs), and population-level summary. However, hybrid clinical/observational research like External Comparators (ECs) leads to new reflections on existing attributes and to considerations for additional ones. Specifically, treatment conditions and exposure may be more difficult to handle in the EC, and especially Standard of Care (SoC) treatment needs detailed attention. The external population typically cannot be based on the classical Intention-to-treat population and constitutes also an approximation only. Endpoints may not be comparable across cohorts, and IEs may be more different than in an RCT setting, such that the hypothetical treatment policy according to the ICH E9(R1) addendum may become of greater interest especially for long-term endpoints. Finally, the necessary assumptions for some population-level summaries (e.g., the proportional hazards assumption) can become more fragile when joining data from different sources due to induced heterogeneity. Finally, it is shown that the baseline definition and the marginal estimator are candidates for additional estimand attributes in case the estimand framework is revised to account for observational study needs.","PeriodicalId":489826,"journal":{"name":"Frontiers in drug safety and regulation","volume":"8 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"External comparators and estimands\",\"authors\":\"Gerd Rippin\",\"doi\":\"10.3389/fdsfr.2023.1332040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The estimand framework as defined by the ICH E9(R1) addendum aims to clearly define “the treatment effect reflecting the clinical question posed by the trial objective”. It intends to achieve this goal of a clear definition by specifying the 5 estimand attributes: treatment conditions, population, endpoints, handling of intercurrent events (IEs), and population-level summary. However, hybrid clinical/observational research like External Comparators (ECs) leads to new reflections on existing attributes and to considerations for additional ones. Specifically, treatment conditions and exposure may be more difficult to handle in the EC, and especially Standard of Care (SoC) treatment needs detailed attention. The external population typically cannot be based on the classical Intention-to-treat population and constitutes also an approximation only. Endpoints may not be comparable across cohorts, and IEs may be more different than in an RCT setting, such that the hypothetical treatment policy according to the ICH E9(R1) addendum may become of greater interest especially for long-term endpoints. Finally, the necessary assumptions for some population-level summaries (e.g., the proportional hazards assumption) can become more fragile when joining data from different sources due to induced heterogeneity. Finally, it is shown that the baseline definition and the marginal estimator are candidates for additional estimand attributes in case the estimand framework is revised to account for observational study needs.\",\"PeriodicalId\":489826,\"journal\":{\"name\":\"Frontiers in drug safety and regulation\",\"volume\":\"8 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in drug safety and regulation\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.3389/fdsfr.2023.1332040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in drug safety and regulation","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.3389/fdsfr.2023.1332040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
ICH E9(R1)附录所定义的估计值框架旨在明确定义 "反映试验目标所提出的临床问题的治疗效果"。该框架旨在通过指定 5 个估计值属性来实现明确定义的目标:治疗条件、人群、终点、并发症(IEs)处理和人群水平总结。然而,外部参照物(ECs)等混合临床/观察研究导致了对现有属性的新思考和对额外属性的考虑。具体来说,治疗条件和暴露可能更难在 EC 中处理,特别是标准护理(SoC)治疗需要详细关注。外部人群通常不能基于传统的 "意向治疗人群",也只是一个近似值。不同队列的终点可能不具有可比性,IE 可能比 RCT 环境中的差异更大,因此,根据 ICH E9(R1) 附录制定的假设治疗政策可能更有意义,尤其是对于长期终点而言。最后,由于诱发异质性,在合并不同来源的数据时,某些人群水平总结的必要假设(如比例危险假设)可能会变得更加脆弱。最后,研究表明,如果对估算模型框架进行修订以满足观察研究的需要,基线定义和边际估算器可作为附加估算模型属性的候选模型。
The estimand framework as defined by the ICH E9(R1) addendum aims to clearly define “the treatment effect reflecting the clinical question posed by the trial objective”. It intends to achieve this goal of a clear definition by specifying the 5 estimand attributes: treatment conditions, population, endpoints, handling of intercurrent events (IEs), and population-level summary. However, hybrid clinical/observational research like External Comparators (ECs) leads to new reflections on existing attributes and to considerations for additional ones. Specifically, treatment conditions and exposure may be more difficult to handle in the EC, and especially Standard of Care (SoC) treatment needs detailed attention. The external population typically cannot be based on the classical Intention-to-treat population and constitutes also an approximation only. Endpoints may not be comparable across cohorts, and IEs may be more different than in an RCT setting, such that the hypothetical treatment policy according to the ICH E9(R1) addendum may become of greater interest especially for long-term endpoints. Finally, the necessary assumptions for some population-level summaries (e.g., the proportional hazards assumption) can become more fragile when joining data from different sources due to induced heterogeneity. Finally, it is shown that the baseline definition and the marginal estimator are candidates for additional estimand attributes in case the estimand framework is revised to account for observational study needs.