What is a Pragmatic Clinical Trial?
Clinical trials can be designed to be either pragmatic or explanatory. Explanatory trials are designed to find out whether a treatment has any efficacy (usually compared with placebo) under ideal, experimental conditions. In contrast to explanatory clinical trials, pragmatic clinical trials (PCTs) are designed to evaluate the comparative effectiveness of interventions within routine clinical settings (1,2). These trials are “pragmatic” because they focus on understanding how interventions work in real life as opposed to “explanatory” where the goal is to determine if and how an intervention works (2).
Why Pragmatic Clinical Trials are gaining popularity?
Explanatory clinical trials, especially randomized controlled trials (RCTs), are designed as experiments with high internal validity—the ability to determine cause-effect relationships. These experiments employ comprehensive designs to control for most, if not all, sources of bias (systematic errors) by means of randomization, blinding, allocation concealment, etc. Usually, extended inclusion and exclusion criteria are used to identify a clearly defined population group of participants who would benefit from the intervention under investigation. Although the above experimental design, if correctly applied, leads to well-controlled trials with statistically credible results; the applicability of these results to real-life practice may be questionable. Indeed, the same characteristics that contribute to the high internal validity of a trial (well-defined inclusion and exclusion criteria, blinding, controlled environment) can hamper its external validity, the ability to generalize the results in an extended population and clinical setting. Although hundreds of trials and RCTs have been performed so far in most clinical conditions, comparing dozens of interventions, there is growing concern that the results obtained from clinical research may not apply to “real-world” situations, because the research is often done under artificial conditions with volunteers who may not reflect the patients who actually live with a given disease or condition (3,4). Analyses of trials contained in the ClinicalTrials.gov database have shown that the vast majority of clinical trials are too small to provide sufficient statistical power to definitively answer clinical questions, they fail to address critical treatment priorities, or they suffer from shortcomings in design and execution that limit their usefulness (4).
The need for high-quality, widely applicable evidence is gaining momentum, especially amidst health care policy makers. The increased costs of interventions and health care in a resource-limited environment have fuelled the demand for clinically effective and applicable evidence (3).
A move to a learning health system is being recommended in which tools such as computing power, connectivity, team-based care, and systems engineering techniques will produce a culture of continuous learning at lower cost. Ideally, clinical trials would be embedded within a system of healthcare delivery where evidence is rapidly and continually fed back into clinical care, and clinical care itself would inform the further development of medical evidence (4, 5). More recently, the growing widespread availability of electronic health record (EHR) data in community practice and advances in information technology, has led to the potential to use such complex sets of data to streamline trials and conduct PCTs (4,6). Pragmatic trials are taking advantage of these settings and conducting research that is specific to the practitioners and patients involved in these complex systems. They use data collected in the EHR as part of routine care, or are “embedded” in routine care, and are a foundational component of such a system (7).
Key attribute differences between traditional RCTs and PCTs (4; NIH Pragmatic Trials Collaboratory)
| Attribute | RCT | PCT |
| Who develops the study questions? | Researchers | Clinical decision makers (patients, clinicians, administrators, and policy makers) |
| What is the purpose? | Create generalizable knowledge; determine causes and effects of treatments | Create generalizable knowledge, improve care locally, and inform clinical and policy decisions |
| What question does it answer? | Can this intervention work under ideal conditions? | Does this intervention work under usual conditions? |
| Who is enrolled? | A cohort of patients with explicitly defined inclusion and exclusion criteria | Diverse, representative populations; inclusion and exclusion criteria still apply, but tend to be broader |
| Who collects data? | Researchers; data collection occurs outside of routine clinical care | Clinicians at the point of care in cooperation with researchers; EHRs and registries are used as sources of research data |
| What is studied? | “A biological or mechanistic hypotheses” | “The comparative balance of benefits, burdens and risks of a biomedical or behavioural health intervention at the individual or population level” |
| What is compared? | Treatment vs placebo or non-treatment | The comparative effectiveness of real-world alternatives |
| Is the study randomized to control for potential biases? | Yes; usually at the individual level | Yes; may use experimental designs and randomization schemes such as cluster randomization (randomization by hospital or unit) or stepped wedge randomization (which involves random crossover of clusters over time from control to intervention until all clusters are exposed |
| What is the setting? | Medical centres designated as research sites | Multiple, heterogeneous settings |
| Adherence to the intervention | Strictly enforced | Flexible (as it would be in usual care) |
| Outcomes | May be surrogates or process measures | “Directly relevant to participants, funders, communities, and healthcare practitioners” |
Pragmatic–explanatory continuum indicator summary – 2 (PRECIS) Framework
Design decisions in domains that determine the extent to which a trial is pragmatic or explanatory need to be made. The proposal for the pragmatic–explanatory continuum indicator summary (PRECIS) was developed by an international group of interested people involved in conducting clinical trials at 2 meetings in Toronto between 2005 and 2008 (8). The PRECIS – 2 tool focuses on trial design choices which determine the applicability of a trial. The nine domains of PRECIS – 2 tool include eligibility, recruitment, setting, organization, flexibility (delivery), flexibility (adherence), follow-up, primary outcome and primary analysis (9).
Published on October 17, 2022
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