Pharma and the benefits of Real World Data 

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Karen Ooms, Executive Vice President and Head of Statistics with Quanticate, argues that analysing data from real-life patients has finally come of age. 

Statistical consultants have, for a long time, tried to convey to the pharmaceutical industry the merit of using real world data (RWD) to help create more efficient trial designs and provide, potentially, even more reliable data to inform clinical trials.  

Despite this championing, many product sponsors and their contract research organisation (CRO) partners have been reluctant to embrace the new research approach, favouring the traditional clinical trial. However, recent changes in the way medical data is collated and made available to the industry, combined with shocks, such as the Covid-19 pandemic, are transforming the way sponsors think about RWD. These developments mean it is time, finally, for pharmaceutical companies to welcome the use of RWD. 

Traditional approaches 

But, why is RWD now becoming so vital to the pharmaceutical industry? The answer lies in the limitations of the traditional randomised clinical trial. Clinical trials have long been central to the drug development process, as they can deliver vital information about the therapeutic effect and safety of a new drug candidate. However, they have shortcomings that reduce the usefulness when understanding the real-life performance of new therapies.   

Their narrow inclusion criteria, for instance, leaves out patients under concomitant treatments, with comorbidities or organ dysfunctions, or over a certain age limit from studies. The intention here is to reduce confounding factors in order to produce data applicable to the average patient. In the real world though, patients taking the therapy may well be taking additional medications to treat other conditions. Not including such patients makes it impossible to gain a full picture of the treatment’s performance.  

In addition, it is challenging to find enough patients for a study to ensure adequate representation of all the possible patients for a particular drug candidate. This issue is particularly acute for treatments for rare diseases, or for demographics where there are ethical concerns, such as children, older people, or pregnant women.  

Another factor that clinical trials are ill-equipped to account for is actual patient adherence. Participants tend to be more compliant with instructions during actual trials than patients in the real world. Ordinary patients may take their dose at different times of the day, or they may forget to take it altogether, when left to their own devices. There may also be struggles with self-administration, which can mean they take a lower than required dose.  

Finally, clinical trials also fail to address varying perceptions shared by healthcare professionals (HCPs) and patients of what constitutes a meaningful impact on symptoms and quality of life. As such, clinical trials are unable to provide a full and detailed picture of the true effectiveness of new treatments. Current healthcare events, such as Covid-19, are driving up demand for real-time data about the efficacy of treatments administered to real patients. This is making it even more important for drug developers to explore alternative sources of data, such as RWD to fill in gaps left by clinical trials.  

 What is RWE and why it is important 

According to the US Food and Drug Administration (FDA) RWD encompasses information “regarding the usage, or the potential benefits or risks, of a drug derived from sources other than traditional clinical trials”.1

This can entail electronic health record data, insurance claims, notes from interactions between doctors and patients, as well as patient-generated information from smartwear devices.  

RWD stored in observational longitudinal databases of multiple patients’ de-identified medical records can be harnessed by expert CROs for in-depth analysis to better understand the effect of treatments and healthcare approaches. The largest of these databases contain information on 100 million patients going back several years – providing far more data than can be acquired during clinical trials alone.  

Unlike with clinical trials, researchers using RWD cannot randomly assign patients to a given therapy nor collect all characteristics that may be of interest – a reason why RWD should be treated as a complement to, not a replacement for, clinical trials.  

Nevertheless, the long-term information offered by RWD makes it possible to track the treatment of rare diseases, treatment pattern changes, and the prescription of therapies for other conditions. Combined with trial data, it is possible to gain a comprehensive view of the therapy’s genuine effectiveness.   

With all of this in mind, it is no surprise that more and more pharmaceutical companies are warming to the use of RWD, and it is already demonstrating its value. In 2019, for instance, Pfizer’s Ibrance was approved by the FDA using analysis largely based on RWD – an industry-first.2

The outlook for RWE 

In spite of its potential to significantly revolutionise our understanding of the treatments we provide patients, the effective use of RWD is still at an early stage. This is because the data itself – what and how much is collected, how and where it is stored, and how it is analysed – is not yet standardised across the globe, or across databases. Each database has its own structure, advantages and limitations, complicating the standarisation process. Administrative healthcare databases, for example, compiled by medical facilities to be sent to insurance companies for billing of private medical care are clean, consistent and standardised. This makes them easier to study, but it restricts the amount of information they offer, as they only have data required by the insurance company.  

Meanwhile, electronic medical records (EMR/EHR) databases, feature patient medical records from across facilities and networks all collated by a single entity. As a result, the information they contain is often disorganised – as multiple approaches to define health events may be used by each originating database – and even incomplete. Despite all of this, they offer in-depth insight into how treatment response may be impacted by the unique attributes of patients, such as lung volume or pain scores. 

Taking all of this into account, it is crucial to use an approach to analysing RWD that is tailored to the individual needs of the database and the treatment. There are expert CROs that can provide the experience and capacity to achieve this goal. Working with such partners, it is possible to effectively capture and study RWD and use it to its full potential.  

RWD has finally come of age 

Drug development projects will always rely on the insight and information delivered by randomised clinical trials. However, RWD, with its rich, long-term data gathered from a wider selection of patients, offers considerable advantages in furthering our knowledge of treatment efficacy.  

Harnessing both approaches together can overcome the traditional limitations of clinical trials in the future, helping us to deliver ever more effective treatments. With the support of expert CROs, pharmaceutical companies can ensure they are using RWD to its full effect, helping them make a measurable impact on patients’ lives. 

Volume 22, Issue 4 – Fall 2021

About the author 

Karen Ooms is Executive Vice President and Head of Statistics at Quanticate,  responsible for overseeing the Statistics department at Quanticate. She is a Chartered Fellow of the Royal Statistical Society and has a background in biostatistics spanning more than 25 years. Prior to joining Quanticate in 1999 (Statwood), she was a Senior Statistician at Unilever. She earned her MSc in Biometry from the University of Reading. 



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