Combining real-world data with randomized controlled trials results in better information oncology decision making - T. A. Usmanova; E. V. Verbitskaya (Реальная клиническая практика: данные и доказательства)
Randomized controlled trials (RCTs) are the gold standard for testing the efficacy of cancer therapy. Although the results of clinical trials have high internal validity, their generalizability, that is, the ability to transfer the results to a wide patient population, is limited. Therefore, users and health care workers may experience less effective intervention in real practice than stated in the RCT. There are many reasons for the formation of a gap between efficacy and effectiveness (efficacyeffectiveness gap; EEG), that is, the measure of impact on RCTs and the real-world. These reasons include, for example, different characteristics of patients in the trial and real practice, compliance to treatment, features of medical care, and others. To illustrate this problem, a review of some studies on the estimation of the magnitude and analysis of the possible causes of this gap is presented. In most of the studies cited, EEG was identified, its probable explanations were proposed, and additional estimates were made to establish the contribution of various factors to its magnitude. These publications» authors show that real-world patients are older, have worse functional status, and have a greater number of comorbidities. They are women mostly and are less likely to complete the treatment they have started or move to the next line of therapy, in contrast to participants in RCTs. Additionally, this article proposes various analytical approaches to determine the weight of the main causal factors in the formation of a discrepancy between efficacy and effectiveness, which can be used in the development of the methodology of relevant studies.Knowing the size of the EEG when using different treatment regimens in their region and understanding the extent to which one or another factor can influence the size of this gap, the clinician will be able to predict the effectiveness of treatment and choose the best therapy for a particular patient.