Multiple Data Sources Show That Decentralized Clinical Trials Pay Off

Traditional clinical trials are typically conducted at central locations or sites that patients must travel to in order to be evaluated and treated by trial investigators. These are referred to as centralized clinical trials.

In contrast, a decentralized clinical trial (DCT) has no set location for patients to report to. Rather, clinical trial activities are moved to more local settings (eg, the patient’s own home), which increases trial accessibility, and digital tools are often used to communicate with study participants and collect data. These types of trials are also referred to as patient-centric trials or virtual trials. Other elements of DCTs include

  • Online recruitment
  • eConsent
  • Wearables
  • Telemedicine visits (eg, remote adverse event reporting)

The incorporation of decentralized elements into clinical trials has been gaining traction since it began becoming more popular during the COVID-19 pandemic, which made it all but impossible to conduct traditional centralized clinical trials.

Since then, regulatory agencies around the globe, particularly in the United States, have shown increasing support for incorporation of DCT elements into registrational clinical trials. Here we summarize 3 reports that focus on the benefits of incorporating decentralized elements into clinical trials vs a traditional clinical trial design.

DCT Approach Can Increase Return on Investment

1) Tufts Center for the Study of Drug Development (CSDD) conducted a modeling study to evaluate the financial benefits of incorporating DCT elements into clinical trials. The results indicated that

  • DCT elements have the greatest impact on reducing clinical cycle times (Phase 2 and Phase 3), and also significantly reduce screen failure rates and protocol amendments
  • Incorporating DCT elements increases return on investment (ROI) by nearly 5-fold in Phase 2 trials and 13-fold in Phase 3 trials

The Tufts CSDD modeling study ultimately found that incorporating DCT elements can increase a drug’s market value by $20 million (if applied in both Phase 2 and Phase 3). The study concluded that DCTs substantially increase financial value based on key performance indicators.

2) IQVIA compared DCTs with more traditional trials. The study reviewed >300 DCTs and selected 12 that completed recruitment. Overall, the analysis showed that sponsors can achieve faster and less expensive clinical trials using DCT-driven protocols. Moreover, the analysis showed that DCT elements contribute to increased patient engagement, meaning that they are more involved and invested in the trial and in their own healthcare decisions. With respect to speed and efficiency, there were substantial reductions in

  • Time from final protocol to first patient in (49%)
  • Accrual time (78%)
  • Screen failure rate (39%)
  • Protocol deviations (54%)

The IQVIA analysis concluded that DCTs deliver a significantly better ROI and offer measurable benefits to sponsors in terms of time and cost efficiencies at virtually every point in the clinical research journey.

DCT Approach Results in Higher Quality Clinical Trials

1) Medidata surveyed 400 clinical trial executives who reported the average number of studies including at least one decentralized element was 43% before the pandemic, the current average is 55%, and the predicted average in 5 years is 66%. All respondents said there were clear benefits to a DCT approach, including improvements in

  • Patient recruitment and retention
  • Patient experience and overall engagement/investment
  • Compliance and governance adherence
  • Data quality
  • Access to real-time data to make real-time decisions

The Medidata report also highlighted potential barriers to conducting DCTs, including

  • Cost and investment in new technologies
  • Training employees in the use of new technologies
  • Relative lack of regulatory guidance on DCTs

The Medidata survey concluded that the COVID-19 pandemic has led to permanent improvements across clinical trial processes that create a more streamlined and efficient operating model and put the patient experience front and center when designing clinical trials.

The overall message conveyed by all 3 analyses is that incorporating decentralized elements into clinical trials can pay off big for Sponsors in multiple ways.

Aaron Csicseri, PharmD, is a clinical pharmacist with 15+ years of experience in medical communications, from developing accredited and promotional medical education to helping clients prepare for FDA Advisory Committee meetings and other health authority interactions. Connect with Aaron on LinkedIn.


Surrogate Endpoints for Accelerated Approval

Surrogate endpoints have been used for accelerated approval (AA) since the early 1990s, playing a vital role in getting therapies for serious conditions to patients sooner. The AA pathway was first created in 1992 to accelerate the approval of drugs intended to treat “serious conditions that fill an unmet medical need.” In the intervening 30+ years, surrogate endpoints have played a major role in oncology and rare disease clinical trials, but their appropriate use is still being debated in the literature. Most often that debate centers around whether an endpoint is a true surrogate that predicts clinical benefit in the clinical context in which it is being used.

What is a “surrogate”endpoint? How is it different from “clinical outcome”endpoint?

A “surrogate” endpoint is a biomarker, lab measurement, radiographic image, physical sign, or other measure that is “reasonably likely to predict clinical benefit” whereas a “clinical outcome” endpoint is one that “directly measures clinical benefit.” Importantly, the FDA definition of clinical benefit is how a patient feels, functions, or survives.

To illustrate the difference between surrogate and clinical endpoints, below are some oncology-specific examples:

Surrogate Endpoints Clinical Outcome Endpoint
Progression-Free Survival (PFS) Overall Survival (OS)
Objective Response Rate (ORR)
Duration of Response (DoR)

The FDA publishes a Surrogate Endpoint Table updated every 6 months and listing surrogate endpoints that can support approval of a drug or a biological product under both accelerated and traditional approval pathways.1 The FDA encourages development of “novel” surrogate endpoints; a novel endpoint can become established as a surrogate based on persuasive evidence that it predicts clinical benefit in the context of a specific disease and patient population. The FDA determines the acceptability of a surrogate endpoint on a case-by-case basis, dependent on context and influenced by the disease, patient population, therapeutic mechanism of action, and currently available treatments (ie, unmet need for new treatments). If a surrogate endpoint was previously used to support AA, but subsequent confirmatory trials consistently fail to demonstrate the expected clinical benefit, that surrogate endpoint should no longer be accepted for that use.

When is it appropriate to use a surrogate endpoint?

The main purpose for using a surrogate endpoint is to shorten clinical development timelines or improve the feasibility of clinical studies in rare diseases where the number of patients is limited and large, controlled studies are challenging. In many cases, a surrogate endpoint can be reached much sooner and with fewer patients than a clinical outcome endpoint such as overall survival (OS), which is a direct measure of clinical benefit. Sponsors must think about this in the context of the specific disease and indication for which they are developing the drug.

For example, in cancer patients with a long life expectancy, a surrogate endpoint such as progression-free survival (PFS) can provide a much earlier readout than a clinical outcome endpoint such as overall survival (OS). If PFS has been shown to correlate with OS in that specific disease and indication, there is a good chance that the confirmatory trial would be able to show an OS benefit. However, in some cases this can be challenging.

In the context of rare genetic diseases, for example, the surrogate endpoint is often a biomarker that can be easily measured with precision and that is reasonably likely to predict how patients feel or function. Because clinical measures of how patients function over time can be difficult to assess with precision, they often require much larger studies to demonstrate a clinically meaningful effect. For example, in Duchenne muscular dystrophy, rather than measuring functional outcomes such as ability to walk, which can vary from one day to the next, researchers will often use a surrogate endpoint such as quantitative measurements of dystrophin protein expression.

In severe respiratory diseases, measures of lung function are often used as a surrogate to predict how well the patient can perform activities of daily living, which can often be difficult to measure with precision. These few examples illustrate how surrogate endpoints can be used to facilitate clinical research.

How much time is saved by using these endpoints?

The amount of time saved by using a surrogate endpoint is disease dependent. For example, use of PFS rather than OS in breast cancer can save almost a full year, whereas the use of response rate (RR) versus OS can save 19 months.2 It all depends on the natural history of the disease and the nature of the endpoint being studied. So, while the use of surrogate endpoints can save time on the front end, and while patients will benefit sooner, the tradeoff is that the sponsor must invest in the development of an additional post-approval confirmatory trial—and there is no guarantee that a direct clinical benefit will be confirmed. Thus, there is a chance that the patient might be taking a drug that turns out not to help them in the long run.

What are “validated” surrogate endpoints?

A “validated” surrogate endpoint meets a higher standard and can be used to support full approval. This requires that the endpoint be “supported by a clear mechanistic rationale and clinical data providing strong evidence that an effect on the surrogate endpoint predicts a specific clinical benefit.”3

Two examples include:

  • HbA1c predicting improvements in long-term complications of type 2 diabetes mellitus
  • Virologic suppression of HIV as a proxy for preventing progression to AIDS

More than 75% of approvals that used a surrogate endpoint came through the traditional pathway using a validated surrogate endpoint.3 The AA pathway does not require the use of validated surrogate endpoints.

Aaron Csicseri, PharmD, Aaron has 10+ years’ experience as a Senior Scientific Director, Medical Director, or Clinical Strategist within the medical communication field. He is responsible for overseeing and developing high-quality scientific and medical content that incorporates key communication objectives and accurate representation of data. Aaron is experienced in the development of strategic scientific communication platforms, strategic publication planning and implementation, medical expert outreach and engagement, guiding and executing medical education programs, and support for medical affairs. He received his PharmD from the University of Buffalo.



  1. US FDA. Table of Surrogate Endpoints That Were the Basis of Drug Approval or Licensure. Updated February 28, 2022. Accessed March 8, 2022.
  2. Chen EY, Joshi SK, Tan A, Prasad V. Estimation of study time reduction using surrogate end points rather than overall survival in oncology clinical trials. JAMA Intern Med. 2019;179(5):642-647.
  3. US FDA. Surrogate Endpoint Resources for Drug and Biologic Development. Updated July 24, 2018. Accessed March 8, 2022.