George Gincy, Russell Beth, Rigg Anne, Coolen Anthony C C, Van Hemelrijck Mieke
Translational Oncology and Urology Research, King's College London, London, UK.
Guy's Cancer Centre, Guy's and St Thomas NHS Foundation Trust, London, UK.
Pragmat Obs Res. 2023 Sep 6;14:95-100. doi: 10.2147/POR.S395959. eCollection 2023.
There is a growing interest in real world evidence when developing antineoplastic drugs owing to the shorter length of time and low costs compared to randomised controlled trials. External validity of studies in the regulatory phase can be enhanced by complementing randomised controlled trials with real world evidence. Furthermore, the use of real world evidence ensures the inclusion of patients often excluded from randomised controlled trials such as the elderly, certain ethnicities or those from certain geographical areas. This review explores approaches in which real world data may be integrated with randomised controlled trials. One approach is by using big data, especially when investigating drugs in the antineoplastic setting. This can even inform artificial intelligence thus ensuring faster and more precise diagnosis and treatment decisions. Pragmatic trials also offer an approach to examine the effectiveness of novel antineoplastic drugs without evading the benefits of randomised controlled trials. A well-designed pragmatic trial would yield results with high external validity by employing a simple study design with a large sample size and diverse settings. Although randomised controlled trials can determine efficacy of antineoplastic drugs, effectiveness in the real world may differ. The need for pragmatic trials to help guide healthcare decision-making led to the development of trials within cohorts (TWICs). TWICs make use of cohorts to conduct multiple randomised controlled trials while maintaining characteristics of real world data in routine clinical practice. Although real world data is often affected by incomplete data and biases such as selection and unmeasured biases, the use of big data and pragmatic approaches can improve the use of real world data in the development of antineoplastic drugs that can in turn steer decision-making in clinical practice.
与随机对照试验相比，由于所需时间较短且成本较低，在开发抗肿瘤药物时，人们对真实世界证据的兴趣与日俱增。在监管阶段，通过用真实世界证据补充随机对照试验，可以提高研究的外部有效性。此外，使用真实世界证据可确保纳入那些通常被随机对照试验排除在外的患者，如老年人、特定种族或来自特定地理区域的人群。本综述探讨了将真实世界数据与随机对照试验相结合的方法。一种方法是使用大数据，尤其是在抗肿瘤环境中研究药物时。这甚至可以为人工智能提供信息，从而确保更快、更精确的诊断和治疗决策。实用试验也提供了一种方法，用于检验新型抗肿瘤药物的有效性，同时又不丧失随机对照试验的益处。精心设计的实用试验通过采用简单的研究设计、大样本量和多样化的环境，将产生具有高外部有效性的结果。虽然随机对照试验可以确定抗肿瘤药物的疗效，但在现实世界中的效果可能有所不同。由于需要实用试验来帮助指导医疗保健决策，因此催生了队列内试验（TWICs）。TWICs利用队列进行多项随机对照试验，同时保持常规临床实践中真实世界数据的特征。虽然真实世界数据常常受到不完整数据以及选择偏倚和未测量偏倚等偏差的影响，但使用大数据和实用方法可以改善真实世界数据在抗肿瘤药物开发中的应用，进而指导临床实践中的决策。
