Clinical Trials vs. Real-World Data: Understanding the Differences and Complementary Roles

Key Takeaways:

 

• Clinical trials are controlled experiments designed to evaluate safety and efficacy of new drugs or devices. Real-world data comes from more diverse, less controlled sources like electronic health records and medical claims.
• Clinical trials have strict inclusion/exclusion criteria and measure predefined outcomes. Real-world data reflects broader populations with various comorbidities and outcomes.
• Clinical trials are required for regulatory approval but have limitations like small sample sizes. Real-world evidence can complement trials with larger volumes of data over longer time periods.
• Real-world data comes from routine clinical practice rather than protocol-driven trials. It provides supplementary information on effectiveness and safety.
• Limitations of real-world data include lack of randomization, potential biases and confounders. Analytic methods help account for these limitations.
• Real-world evidence has growing applications in medical product development, post-market surveillance, regulatory decisions and clinical guideline development.

Clinical Trials vs. Real-World Data

Clinical trials are prospective studies that systematically evaluate the safety and efficacy of investigational drugs, devices or treatment strategies in accordance with predefined protocols and statistical analysis plans. They are considered the gold standard for assessing the benefits and risks of medical interventions prior to regulatory approval. In clinical trials, participants are assigned to receive an investigational product or comparator/placebo according to a randomized scheme. These studies are designed to minimize bias and carefully control variables that may affect outcomes. Participants are closely monitored per protocol, and data is collected on prespecified points in time. The resulting evidence from randomized controlled trials serves as the primary basis for regulatory decisions regarding drug and device approvals.

 

In contrast, real-world data (RWD) refers to data derived from various non-experimental or observational sources that reflect routine clinical practice. Sources of RWD include electronic health records (EHRs), medical claims, registry data and patient-generated data from mobile devices, surveys or wearables. Real-world evidence (RWE) is the clinical evidence generated from aggregation and analysis of RWD. While clinical trials evaluate medical products under ideal, controlled conditions in limited samples of patients, RWD offers information about usage, effectiveness and safety in broader patient populations in real-world settings.

 

Some key differences between clinical trials and real-world data:

Sample Populations – Clinical trials have strict inclusion and exclusion criteria, resulting in homogeneous samples that often under represent minorities, elderly, pediatric and complex patient groups. RWD reflects more diverse real-world populations with various comorbidities and concomitant medications.

 

• Settings – Clinical trials are conducted at specialized research sites under tightly controlled conditions. RWD comes from routine care settings like hospitals, clinics and pharmacies across diverse geographies and populations.
• Interventions – Clinical trials administer interventions per protocol. RWD reflects variabilities in real-world treatment patterns and patient adherence.
• Outcomes – Clinical trials measure prespecified outcomes over limited timeframes. RWD captures broader outcomes like patient-reported outcomes, quality of life, hospitalizations and costs over longer periods in real-world practice.
• Data Collection – Clinical trials collect data per protocol at predefined assessment points. RWD is collected during routine care and reflected in patient records and claims.
• Sample Size – Clinical trials often have small sample sizes with a few hundred to several thousand patients. RWD encompasses data from tens or hundreds of thousands of patients.
• Randomization – Clinical trials use randomization to minimize bias when assigning interventions. RWD studies are observational without the benefits of randomization.

 

While randomized controlled trials provide high quality evidence for drug/device approvals and clinical recommendations, RWD offers complementary information on effectiveness, safety, prescribing patterns and health outcomes:

 

• RWD can provide broader demographic representation for subpopulations underrepresented in trials.
• RWD can inform on long-term safety, durability of treatment effects and comparative effectiveness between therapies.
• RWD can provide larger sample sizes to study rare events or outcomes.
• RWD can reflect real-world utilization rates, switching patterns and adherence to therapies.
• RWD offers granular data for personalized medicine, risk identification, prediction modeling and tailored interventions.
• RWD is more timely, cost-effective and scalable than conducting large trials.

 

However, RWD has inherent limitations compared to clinical trials:

 

• Lack of randomization increases potential for bias and confounding.
• Incomplete data or misclassification errors are common with medical records.
• Inability to firmly conclude causality due to observational nature.
• Possible selection biases and variations in care delivery across settings.
• Inconsistencies in definitions, coding, documentation practices over time and sites.

 

Analytical methods help account for these limitations when generating real-world evidence from RWD:

 

• Advanced analytics like machine learning can identify trends and associations within large RWD.
• Predictive modeling and simulations can estimate treatment effects.
• Adjusting for confounders, stratification, matching patients, propensity scoring help reduce biases.
• Expert review of data and methodology helps ensure reliability.

 

Applications of RWE are expanding and gaining acceptance from key stakeholders:

 

• Supplement clinical trial data for regulatory, coverage and payment decisions around medical products.
• Post-market surveillance of drug and device safety and utilization in real-world practice.
• Life cycle evidence generation for new indications, formulations, combination products.
• Provide inputs into clinical guidelines by professional societies.
• Risk identification/stratification, predictive modeling and personalized medicine.
• Value-based contracting between manufacturers and payers.
• Risk management and safety programs for hospitals and health systems.

 

In summary, clinical trials provide foundational evidence to introduce new medical products, while RWE offers complementary insights on effectiveness, safety, prescribing patterns and health outcomes at a larger scale across diverse real-world populations. Advanced analytics help derive meaningful RWE from RWD, with growing applications across the healthcare life science ecosystems. Together, these sources of evidence offer a multifaceted understanding to guide optimal use of medical products and improve patient care.

 

Sources:

1. ClinicalTrials.gov. What are the different types of clinical research? https://clinicaltrials.gov/ct2/about-studies/learn#WhatIs
2. Berger ML, et al. Real-World Evidence: What It Is and What It Can Tell Us According to the ISPOR Real-World Data Task Force. Value Health. 2021 Sep;24(9):1197-1204.
3. Sherman RE, et al. Real-World Evidence – What Is It and What Can It Tell Us? N Engl J Med. 2016 Dec 8;375(23):2293-2297.
4. Yu T, et al. Benefits, Limitations, and Misconceptions of Real-World Data Analyses to Evaluate Comparative Effectiveness and Safety of Medical Products. Clin Pharmacol Ther. 2019 Oct;106(4):765-778.
5. Food and Drug Administration. Real-World Evidence. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence