The need for increased pragmatism in cardiovascular clinical trials

Abstract

The majority of cardiovascular randomized controlled trials (RCTs) test interventions in selected patient populations under explicitly protocol-defined settings. Although these 'explanatory' trial designs optimize conditions to test the efficacy and safety of an intervention, they limit the generalizability of trial findings in broader clinical settings. The concept of 'pragmatism' in RCTs addresses this concern by providing counterbalance to the more idealized situation underpinning explanatory RCTs and optimizing effectiveness over efficacy. The central tenets of pragmatism in RCTs are to test interventions in routine clinical settings, with patients who are representative of broad clinical practice, and to reduce the burden on investigators and participants by minimizing the number of trial visits and the intensity of trial-based testing. Pragmatic evaluation of interventions is particularly important in cardiovascular diseases, where the risk of death among patients has remained fairly stable over the past few decades despite the development of new therapeutic interventions. Pragmatic RCTs can help to reveal the 'real-world' effectiveness of therapeutic interventions and elucidate barriers to their implementation. In this Review, we discuss the attributes of pragmatism in RCT design, conduct and interpretation as well as the general need for increased pragmatism in cardiovascular RCTs. We also summarize current challenges and potential solutions to the implementation of pragmatism in RCTs and highlight selected ongoing and completed cardiovascular RCTs with pragmatic trial designs.

Fig. 1. The PRECIS-2 wheel.

A visual representation of how pragmatic a trial is on the explanatory–pragmatic continuum. For each of the nine domains, the level of pragmatism can range from 1 (most explanatory, least pragmatic) to 5 (least explanatory, most pragmatic). Adapted with permission from ref., BMJ.

Fig. 2. Relative likelihood of biases and limitations in each type of clinical study.

Biases might not always be present, and each study should be individually assessed. Lack of generalizability: limited representation of patients who would receive the intervention and providers who would deliver the intervention in routine practice. Hawthorne bias: change in behaviour or perceived effect in patients as a result of awareness of being observed. Confounding bias: a distortion in the measure of the association between an exposure and a health outcome as a result of extraneous factors that are independently associated with both the exposure and the outcome. Prevalent user bias: occurs when users and non-users of a study intervention are compared without a fixed ‘time zero’; patients who start or continue using a particular intervention are likely to differ in characteristics from non-users or those who discontinue treatment. Immortal time bias: patients in the treatment group are more likely to have longer survival times or less serious disease than those in the control group because, owing to the study definition, patients in the treatment group cannot experience the outcome in the period between the start of the study and the initiation of treatment. Observer bias: a researcher’s expectations, opinions or prejudices influence what they perceive or record in a study; can occur in the absence of blinding.