Traditionally clinical trials in the pharmaceutical industry are designed to provide accurate answers to narrowly defined questions. What happens in routine clinical practice can be quite different. Using real world data in our clinical studies can help us to bridge the gap.

One of our S-cubed statisticians, Elizabeth Merrall, tells us more:

Using routinely collected data in healthcare settings has always appealed to me for its convenience. I’m thrilled to see that pharmaceutical regulators are opening up to the wider use of such data to generate evidence of the effectiveness and safety of pharmaceutical products. According to the 21st Century Cures Act signed into law in late 2016, the U.S. food and drug administration (FDA) has since developed a framework for evaluating the potential use of evidence generated from such real-world data (RWD) to support the approval of a new indication for already-approved drugs or to help support or satisfy postapproval study requirements. While the scope for the use of RWD here may sound quite limited, further uses for RWD in pharmaceutical development are recognized within the framework (US FDA, 2018).

What are Real World Data?

Real world data (RWD) refer to health data collected on a routine basis, be it related to the health status of a patient or the delivery of a treatment or care. RWD encompass a range from electronic health records (EHRs), claims and billing data, registries embedded within clinical practice, through to more in-home-use settings and other sources that can inform on health status – such as electronic devices, software apps and social media.

Real World Data in EHRs

Study designs for RWD – can be either experimental or observational

The possibilities are vast and call for study designs beyond the gold-standard randomized controlled trials (RCTs) that are most familiar prior to regulatory approval. Whilst most familiar, RCTS are also highly labour-intensive requiring major infrastructure that is completely separate from routine clinical practice – namely, detailed study protocols and eligibility criteria, case report forms, monitoring and specially-trained research personnel ensuring adherence to the protocol and as a result, top quality data collection.

Some clinical trials may be classified as hybrids, using RWD extracted from EHRs, or laboratory and pharmacy databases, to derive certain endpoints alongside more traditionally collected endpoints (with protocol-specific monitoring and collection by dedicated study personnel). Some randomized designs may be described as pragmatic as they seek to mimic or include aspects that resemble more routine clinical practice and will often rely on RWD.

Many study designs using RWD will be without randomization altogether. These observational designs are already extensively used in the monitoring and evaluation of the safety of drug products after approval. At the same time, they are also recognized for their limitations in terms of the quality and completeness of data available and evidence generated. Nevertheless, statistical methods have been developed to address the potential biases and confounding associated with these data.

Real World Data Study Design

Analysis approaches for RWD – an example, propensity score matching

When looking to compare treatments A and B in an RWD setting, there may be systematic differences between patients receiving treatment A versus treatment B. These differences may be with respect to disease, more severe or longer duration of disease; demographically, such as geography, age, sex and socio-economic status, or even the delivery of the treatment, such as healthcare setting. A popular way to try to address these baseline differences between treatment groups is propensity score matching. Based on available baseline variables, this method seeks to match and compare patients that look most similar. For the analysis, each patient is scored according to his/her propensity to receive treatment A versus treatment B on the basis of his/her baseline characteristics. Patients receiving treatment A (or B) can then be matched or grouped with patients receiving treatment B (or A) that have the most similar scores; or patients can be weighted by the odds of receiving treatment A vs B (higher odds=higher weight), and then analysed accordingly.

RWD Propensity Scores

Examples of using RWD in Regulatory Applications

In recent discussions between the FDA, industry and academia, examples of the use of real world evidence (i.e. evidence generated from RWD or RWE) in regulatory decisions were presented and can be seen here. These examples all relate to single arm trials and therapeutic areas where either no other treatments are available (known as compassionate-use or expanded access) or the disease is rare. They are either pragmatic in nature or use RWD to derive a suitable control group or both. As mentioned previously, the FDA framework highlights further uses of RWD for improving the efficiency of clinical trials, namely:

  • Generating hypotheses for testing in randomized controlled trials
  • Identifying drug development tools (including biomarker identification)
  • Assessing trial feasibility by examining the impact of planned inclusion/exclusion criteria in the relevant population, both within a geographical area or at a particular trial site
  • Informing prior probability distributions in Bayesian statistical models
  • Identifying prognostic indicators or patient baseline characteristics for enrichment or stratification
  • Assembling geographically distributed research cohorts (e.g., in drug development for rare diseases or targeted therapeutics)


As we become increasingly comfortable with RWD, its associated analysis approaches and benefits, I am sure that this list will grow. It’s definitely an exciting area to watch within pharmaceutical developments.

About the Author:

Elizabeth is part of a team of statisticians at S-cubed that provide statistical expertise and support to pharmaceutical and biotech clients, throughout all phases of development. If you’re interested in discussing the use of real world data in your clinical studies, or statistical aspects of the design, analysis and reporting of your clinical studies or submissions, more generally, please feel welcome to reach out to us through our Contact Page.


  1. U.S. Food and Drug Administration (2018) Framework for FDA’s Real-World Evidence Program. Retrieved from: