Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands.
Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Curr Treat Options Oncol. 2024 Apr;25(4):405-426. doi: 10.1007/s11864-024-01186-4. Epub 2024 Feb 17.
Treatment guidelines for colorectal cancer (CRC) are primarily based on the results of randomized clinical trials (RCTs), the gold standard methodology to evaluate safety and efficacy of oncological treatments. However, generalizability of trial results is often limited due to stringent eligibility criteria, underrepresentation of specific populations, and more heterogeneity in clinical practice. This may result in an efficacy-effectiveness gap and uncertainty regarding meaningful benefit versus treatment harm. Meanwhile, conduct of traditional RCTs has become increasingly challenging due to identification of a growing number of (small) molecular subtypes. These challenges-combined with the digitalization of health records-have led to growing interest in use of real-world data (RWD) to complement evidence from RCTs. RWD is used to evaluate epidemiological trends, quality of care, treatment effectiveness, long-term (rare) safety, and quality of life (QoL) measures. In addition, RWD is increasingly considered in decision-making by clinicians, regulators, and payers. In this narrative review, we elaborate on these applications in CRC, and provide illustrative examples. As long as the quality of RWD is safeguarded, ongoing developments, such as common data models, federated learning, and predictive modelling, will further unfold its potential. First, whenever possible, we recommend conducting pragmatic trials, such as registry-based RCTs, to optimize generalizability and answer clinical questions that are not addressed in registrational trials. Second, we argue that marketing approval should be conditional for patients who would have been ineligible for the registrational trial, awaiting planned (non) randomized evaluation of outcomes in the real world. Third, high-quality effectiveness results should be incorporated in treatment guidelines to aid in patient counseling. We believe that a coordinated effort from all stakeholders is essential to improve the quality of RWD, create a learning healthcare system with optimal use of trials and real-world evidence (RWE), and ultimately ensure personalized care for every CRC patient.
结直肠癌（CRC）的治疗指南主要基于随机临床试验（RCT）的结果，这是评估肿瘤治疗安全性和疗效的金标准方法。然而，由于严格的入选标准、特定人群代表性不足以及临床实践中的更多异质性，试验结果的普遍性往往受到限制。这可能导致疗效-效果差距和对治疗益处与危害的不确定性。同时，由于越来越多的（小）分子亚型的确定，传统 RCT 的开展变得越来越具有挑战性。这些挑战——加上健康记录的数字化——导致人们越来越有兴趣使用真实世界数据（RWD）来补充 RCT 的证据。RWD 用于评估流行病学趋势、护理质量、治疗效果、长期（罕见）安全性和生活质量（QoL）指标。此外，RWD 在临床医生、监管机构和支付方的决策中也越来越受到重视。在本叙述性综述中，我们详细阐述了 RWD 在 CRC 中的这些应用，并提供了说明性示例。只要 RWD 的质量得到保障，正在进行的开发，如通用数据模型、联合学习和预测建模，将进一步发挥其潜力。首先，只要有可能，我们建议进行实用临床试验，例如基于登记的 RCT，以优化普遍性并回答注册试验中未涉及的临床问题。其次，我们认为，对于不符合登记试验入选标准的患者，应在等待对真实世界结局进行计划（非）随机评估的情况下，有条件地批准上市。第三，应将高质量的效果数据纳入治疗指南，以帮助患者咨询。我们认为，所有利益相关者的协调努力对于提高 RWD 的质量、创建一个最佳利用试验和真实世界证据（RWE）的学习型医疗保健系统以及最终确保每位 CRC 患者获得个性化护理至关重要。
