https://orcid.org/0000-0002-7317-0858
Abstract
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.
Abbreviations
AI, artificial intelligence; EHR, electronic healthcare record; COS, core outcome sets; CDM, common data models; COMET, core outcome measures in effectiveness trials; IMI, innovative medicine initiative; BD4BO, big data for better outcomes; OHDSI, observational health data sciences and informatics; OMOP, observational medical outcomes partnership; TWICs, trials within cohorts; PRECIS, pragmatic-explanatory continuum indicator summary; UMBRELLA, Utrecht cohort for Multiple BREast cancer intervention studies and Long-term evaLuAtion.
Disclosure
The authors report no conflicts of interest in this work. This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.