Towards Data-Driven Clinical Trial Planning and Strategy

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Applied Clinical Trials, Applied Clinical Trials-02-01-2022, Volume 31, Issue 1/2

Optimizing feasibility through increased data collection.

The biopharma industry continues to improve its ability to implement data-driven decision making into drug development. As more data is collected, cleaned, and structured, companies are building advanced analytical capabilities to optimize trial feasibility and predictability, operational plans, and resource allocation. In order to have an objective view to ensure efficient and meaningful trials, there must be an optimal number of quality patients, sites, and investigators who are willing and capable to take part in the trial. This willingness also depends very much on the nature of the trial, the drug under investigation, and the study design, including the patient experience. Clinical trials are conducted on a global scale, and yet, the trial journey for any given patient is very much a personal decision. From the site’s and patient’s perspective, there are more clinical trials than ever, resulting in greater choice and selectivity. This also means that competition for sites and patients continues to increase for pharma sponsors. Thus, understanding geographical, clinical, operational, and medical practices are critical to a clinical trial’s success. Sponsors are responding by increasing their capabilities to perform robust data-driven feasibility to create operational plans that will increase the likelihood of enrolling the trial on time, within budget, and with a high level of quality.

Data-driven analytics and decision making benefit the clinical trial feasibility process. Conducting clinical trials requires extensive resources, and ensuring the operational plan is feasible is imperative to maintain and potentially reduce the costs of drug development. Insights from the data can provide opportunities to introduce efficiencies to clinical trial design and execution planning while maintaining or exceeding the required regulatory standards. With regards to data, there are several considerations: (1) the types of data that are critical to optimally predict and forecast trial operations; (2) the sources, partnerships, and prior experience that are required to acquire this data; and (3) producing meaningful insights from the data to support better decision making. Data quality is important and is highly dependent on the source—not all data has the same integrity. This paper will explore the evolution of clinical trial feasibility at biopharma companies and expound on the current challenges going into the future. Towards an improved clinical trial feasibility process, ZS’s Clinical Trial Feasibility Consortium will be introduced—this Consortium serves as a think tank focusing on clinical feasibility challenges in R&D, including role accountabilities and organizational structures, talent engagement and retention, and data and analytical capability requirements.

 Although there are variations in organizational structure, generally each biopharma company has individuals, a structured group, or an outside organization (e.g. contract research organizations/ CROs) with accountability for clinical trial feasibility. The function evaluates the possibility of conducting a particular clinical program or trial with an optimal project completion in terms of timelines, site and patient targets and cost. The type of work that these feasibility groups perform can be summarized in the figure below.

The evolution of clinical feasibility

From surveying and interviewing the Consortium members, two-thirds of our Consortium members initiated their assessments more than 3 months ahead of study start date, with an average of 7 month lead time for assessing study feasibility. The process involves selecting countries, sites, and investigators that have the best potential to enroll the target number of patients within the shortest amount of time. Even though feasibility is not a new practice for the pharmaceutical industry, components and methodologies have continuously evolved over the past few decades, particularly with the explosion of new data and computational power for data analytics.

 In the late 20th century, feasibility was heavily dependent on a company’s local teams or CROs. The local affiliates made recommendations or decisions based on their prior experience and relationships with sites and investigators. Since local knowledge represented expertise accumulated over years or decades of experience, it remained an irreplaceable component of the process even as more data became available. However, its empirical nature made the selection process too subjective to be standardized, leading to inconsistencies and inefficiencies over time. It also meant that new sites and investigators had a very difficult time getting onto the “list” of considerations.

 At the turn of the century, insights from “hard” data gradually became incorporated into the feasibility process. When government-sponsored clinical trial registries like, EudraCT, and Japan UMIN-CTR were established in the early 2000s, vendors such as Citeline began to process, standardize, and integrate data from these various sources. These aggregators soon constructed a centralized location for sponsors to access site and investigator information and performance tiers. Simultaneously, disease epidemiology and market sizing data provided by research organizations such as Decision Resources Group (DRG) helped sponsors make informed decisions on country and region selection for their trials. At this time, sponsors also began to interrogate their internal data looking for countries and sites that had performed well in previous studies, incorporating what they’ve learned from their competitors through these public aggregators.

 Today, as more trial, site, investigator, and patient data comes online, and as advanced data analytics becomes more accessible and commonplace, there is a concerted effort to push towards data-driven clinical trial planning. For instance, the availability of digital Real-World Data (RWD), including Electronic Health Records (EHR) and insurance claims, has made it easier to locate patients with certain indications or with specific variations of an indication. Increasing the frequency of reporting and technology-enabled continuous reporting has led to a boom in the amount of data available to biopharma companies. Additionally, sponsor companies are now investing in data science and analytics capabilities to gain even deeper insights into their experience (enrollment, startup, and quality) with sites. Overall, the feasibility process has essentially transitioned from local experiences and relationships to a data-driven approach with some sponsors building clinical trial design studios, centrally led but with input gathered from and by local expertise. Both biopharma companies and CROs continue to develop custom predictive analytics that leverage more types of data, statistical algorithms, and machine learning methods to generate even greater insights from the data.

Current challenges in clinical trial feasibility

Lack of available data outside the United States

Over the next decade, we foresee biopharma companies continue to tackle key challenges in obtaining more targeted data and building more effective analytical pipelines. This is because we see the industry trending towards personalized and precision medicine. We expect that data outside the US continues to remain limited or of inconsistent quality, and within the US, specialty data may also lag behind the demands of precision medicine. For example, there continues to only be a targeted use of EHRs due to limitations in granular data needed for things like cancer staging or mutation status. However, RWD will continue to have a high integration potential—with vast amounts of data now available and increased competition, many sponsor companies are looking to hire individuals with artificial intelligence (AI), machine learning (ML), and deep learning (DL) backgrounds to work on their feasibility teams. They are the ones with experience to help process and create meaningful insights of the data collected. One of the goals of the Consortium is to bring about improvements in data-driven strategic insights.


Biopharma companies invest in the development of their products for the wellbeing of patients around the world. A necessary step to achieve this goal is to conduct clinical trials worldwide. Particularly when US/EU-based pharma companies step outside of the US to launch clinical trials, however, the quality and quantity of data that can be used for site feasibility analyses in European, Asian, and African countries are not comparable to those of the United States. In the EU, strict privacy laws such as the GDPR assign new rights and ownership to data and may require systemic rethinking of data handling and security. Not only does the patient’s records need to be secured, but now, one must be prepared to hand them over or destroy them at will, leading to incomplete or potentially misleading data. In China and Japan, language barriers, as well as a variety of local regulations, such as China’s Human Genetic Resources Regulation, hinder data standardization and centralization. And in Africa, countries are just starting to work on enhancing or building infrastructure for rigorous data collection. The uneven data landscape leads to a higher degree of variability in the feasibility process for ex-US clinical trials, and it largely depends on a company’s ability to access data in their countries of interest. In Figure 2 below, Consortium members note which countries traditionally have good, high quality data versus inconsistent or no data.

Opportunities for real-world data (RWD) usage and intake

During our interviews with Consortium members, they acknowledged that real-world data could elevate several aspects of clinical trials and complement existing data sets. For example, claims data can be used to identify areas with high disease prevalence and incidence rates, and EHR can elucidate disease treatment patterns and standards of care. In fact, some feasibility groups have begun to use RWD platforms such as TriNetX and Flatiron to bolster their protocol design assessments, and country and site recommendations. As these RWD platforms continue to expand and include representation of healthcare organizations from additional regions of the world, the outputs from these tools will become even more informative over time. There continues to be challenges in adding real-world data into existing algorithms and models, and outputting meaningful forecasts and predictions. For sponsors, key stakeholders of the clinical feasibility process struggle with the value proposition of real-world data, particularly when factoring in the price and time it takes to procure and use them.

Growing needs for specialty data

The increasing maturity and decreasing cost of genetic and protein sequencing techniques has brought precision medicine closer to patients. For biopharma companies with an extended oncology portfolio, patient genomic data has become an asset that helps categorize patients into more specific categories of common indications, such as non-small cell lung cancer or breast cancer. The potential for targeted therapies can result in a smaller sample size, but depending on the biomarker sub-population, the patients can be more difficult to find. This can result in longer enrollment durations and/or more sites needed for the study. In many cases, the sites also need to work harder (eg: screen 10 potential patients to enroll three patients) to find eligible patients. In cases where the biomarker is identified as part of the standard of care panel and when the biomarker positive patients have an unfavorable prognosis, the studies can enroll incredibly quickly. Having access to this data empowers sponsors to quickly reach their target patients at lower costs. Although genomic testing is considered to be standard for many types of cancers, general access to this genomic data continues to be limited, though companies such as Tempus and Foundation Medicine are now beginning to sell de-identified data sets. There is an incredible opportunity for scaling up the usage of genomic data in the next few years.

Unmet analytical capabilities and continuous improvement

Even with the explosive increase in data quantity, strategic insights and recommendations cannot be improved unless analytical capabilities continue to evolve based on feedback. Building this analytical machine can be a demanding process for most biopharma companies, as such work requires expertise in data science, mathematical and statistical modeling, and programming. And while there is a growing trend for sponsors to mine and perform advanced analytics on their own internal data, many Consortium members expressed their concerns with the shortage of talent with such advanced skills, due to competition with tech companies (and other industries)—it is even more difficult to find those with those talents and are experienced in the business context of clinical feasibility. After developing these analytical processes, measuring their performance against a standard benchmark is another challenge. Various factors such as the long span of clinical trials, the inability to make direct trial comparisons (due to trial specifics such as patient inclusion and exclusion criteria), and difficulty in quantifying the influence of the feasibility process on the outcome can impede the formation of an effective feedback cycle.

Introducing ZS’s clinical trial feasibility consortium

ZS’s Clinical Feasibility Consortium was founded in June 2019, after our clients began to notice changing trends and expectations with the rapid growth of data. There was a collective interest in forming a Consortium to address some of the unique challenges and to investigate the meaning and role of Clinical Feasibility groups in the era of data science, advanced analytics, and technology. This included aspects such as branding, reputation and change management, and creativity and talent engagement within their organizations. Since inception, the Consortium has produced an industry benchmarking report on current practices for these feasibility groups, with an additional focus on data sources and processes.

There are currently a dozen members who have a significant level of responsibility regarding clinical trial feasibility and/or trial operations at their respective companies. These companies include AbbVie, Amgen, AstraZeneca, BeiGene, Bristol-Myers-Squibb, EMD Serono–Merck KGaA, Gilead, Janssen–Johnson & Johnson, Eli Lilly, Novartis, Regeneron, and Sanofi, a mix of both medium and large-sized pharma companies. As we publicize this group, we welcome all those who are involved and/or have experience with: (1) data-driven feasibility/ operations processes, (2) innovations in trial execution, (3) clinical feasibility challenges in R&D, and others, to join the Consortium.

 All of the proposed solutions to the challenges mentioned above have been discussed at one point or another during the course of one in-person meeting and several teleconferences, and there has been a warm reception towards many of these collaborative ideas. In 2021, our Consortium has collectively decided to tackle the specific challenges of: (1) implementing standards for virtual and decentralized clinical trials, (2) promoting patient centricity at clinical sites and reducing patient burden in trial protocols, and (3) creating feasibility metric tools to gauge the likelihood of clinical trial success. These initiatives closely mirror the word cloud shown in Figure 3, as Feasibility groups shift away from pure analytics and design and more towards patients, strategy, and promoting virtual trials. Perhaps coincidentally, this word cloud was generated before the COVID-19 pandemic, and it stands to reason that the need to virtualize and decentralize clinical trials takes on a greater urgency today than ever before.

Consortium members have pledged to continue to work together to find solutions that make clinical trials more efficient and effective, and ZS will function as an intermediary when it comes to data sharing, company benchmarking, and advocating for causes taken up by the Consortium. Our hope for this Consortium is for Clinical Trial Analytics executives to prepare their organizations and professions to take ownership and prove leadership in this new data-driven world.

Andrew Hsu* is a Strategy Insights & Planning Consultant; Jiangyuan Luo* is a Decision Analytics Associate Consultant; and Jonathan Rowe is a Principal, all for ZS Associates. Jade Dennis is the Clinical Design, Delivery & Analytics Advisor for Eli Lilly and Company. Sandra Smyth is the Director Global Feasibility and Site Intelligence, AstraZeneca. and the ZS Feasibility Consortium.

* - co-first authors