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Tracking Global COVID-19 Clinical Trials

Tracking Global COVID-19 Clinical Trials content piece image
Cytel's Global COVID-19 Trial Tracker
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In efforts to find efficacious interventions against SARS-CoV-2, the novel coronavirus that causes COVID-19, hundreds of clinical trials have been designed and launched at an unprecedented rate across the globe. As data emerges from these studies there is an urgent need to track progress, to ensure transparent communication of findings, and to avoid duplication of efforts.

Technology Networks
spoke with Cytel’s Edward Mills, Vice President of Real-World Evidence and Senior Principal Scientist, to learn more about the global COVID-19 Trial Tracker the company has recently launched. Mills also discusses how current restrictions have impacted our ability to collect, analyze and interpret clinical data, and what can be done with existing data that has been collected from studies that have been disrupted or discontinued as a result of the pandemic.

Laura Lansdowne (LL): For our readers who may be less familiar with Cytel, could you tell us a little more about the company?

Edward Mills (EM):
Cytel employs a range of data science tools, from biostatistics to machine learning, to help executives, in the life sciences industry, make confident decisions powered by data. We are probably best known for being leaders in the field of adaptive clinical trial design, a subset of trial design that uses interim looks to enhance the patient safety and commercial value of pharmaceutical products. We also have specialists in Bayesian statistics, real-world evidence, artificial intelligence, health economics, and a number of other research fields to ensure that academic and scientific findings can have an impact on industry quickly and seamlessly.

LL: How is Cytel addressing the COVID-19 pandemic, can you tell us more about the global COVID-19 clinical trial tracker you recently launched?

Our statisticians are involved in epidemiological modeling, network meta-analysis, and other quantitative strategies that enable decision-makers to take all the data being collected and understand how best to interpret and apply it quickly. The Cytel COVID-19 Trial Tracker, for example, is first and foremost a visualization tool that helps people to quickly identify the number of trials taking place, the therapies involved and what the results are showing. Given the current high level of activity, we wanted to make sure that research is not being replicated and that data across studies can be aggregated, so as to glean as much understanding as possible. This tool will facilitate collaboration amongst scientists and will also ensure that investors and philanthropists know what studies are being conducted, and so can put resources towards the most promising and critically necessary therapies.

LL: How do you identify registered trials and how frequently is the tracker updated?

We have been using trial registries from several countries across the world. Our team updates the COVID-19 Trial Tracker daily to add newly registered trials, while also monitoring existing trials in real time. We have colleagues and partners working on trials across the world, so we are well-positioned to know about exciting new trials happening elsewhere.

LL: Can you touch on the importance of collaboration at this time?

There is an unprecedented level of collaboration in the pharmaceutical industry right now, possibly because we have such clear insight as to how massive the stakes are. There have of course been urgent calls to action before, such as for Ebola, but this feels utterly different. The World Health Organization was able to launch a multinational trial in less than two monthssomething that under normal circumstances would have taken several months longer. The private sector is collaborating in a way that it normally would not, and of course academia and philanthropy are also operating with “all hands on deck”.

LL: How has the COVID-19 pandemic impacted existing clinical trials?

Biostatisticians in our Strategic Consulting unit are being called upon to advise on trials that have been disrupted during the COVID-19 pandemic. The medical sector is already burdened with patients and, as a result, the continuation of many trials has been, or is being, reconsidered. Additionally, there are patients who do not wish to go to hospitals for treatments because of anxieties over becoming infected, and we certainly would advocate for a safety-first principle.

This leads to the natural question of what can be done with the data that has already been collected. Statisticians have an entire sub-discipline on missing data that explains what to do in such cases. Clients are asking how they can determine which clinical endpoints they should be striving towards in this environment. Choosing new endpoints midway through a trial is always risky. You don’t want to do this simply to be strategic, simply to have something to submit to regulators. Sometimes, though, there are legitimate reasons for saying that the four endpoints you were observing ought to be three, and statisticians can help you examine whether or not that is the case.

We are also seeing more Bayesian designs in updated Statistical Analysis Plans. Bayesian designs enable people to make more use of the smaller amounts of data coming their way, so that they can, for example, stop a trial the moment they believe they have sufficient knowledge to understand the outcome.

Another challenge that companies are facing is how to reforecast trials, something that requires more factors than we initially realize. There are, of course, forecasting tools like Monte Carlo simulations that our quantitative strategists employ. However, there is an open question of how to determine which assumptions to begin with when making those forecasts. Some patients will avoid doctors at all costs due to anxieties, others will be more likely to stay engaged because of those same anxieties. How do we tell how people will behave?

LL: How will the current restrictions as a result of COVID-19 impact our ability to collect, analyze and interpret clinical data?

Data collection is becoming tougher so we need to gain as much knowledge as we can from the data that we already have. This might mean different things for different trials, but amongst the discussions we have had at Cytel, we have been looking particularly closely at Bayesian methods for trial design, for network analysis, and also for health economics outcomes research. Perhaps this will lead to an industry paradigm where people are also more likely to share their proprietary data sets. While it is currently too early to tell, that could additionally drive us further towards big data and machine learning.

LL: Do you think that the pandemic will influence the way we design and conduct trials in future, once normal “day-to-day” activities are resumed?

There are certain changes that we are bound to see. For example, these large multinational trials open the way for basket and umbrella designsplatform trials with several moving parts. Investigators in smaller trials are also learning that there is an upside to collaboration. Maybe we will see more of that as well. Some pharmaceutical executives may have to learn more about conducting trials in the Global South, because low- and middle-income countries have a huge role to play during the COVID-19 pandemic. This knowledge of supply chains, distributional channels, and other aspects of operations might mean that clinical development aligns more with global health priorities. These are all possibilities. As far as how data is used and assessed, our mission is to show people that high statistical rigor remains possible even under conditions where there is pressure to find a vaccine quickly. It does require an understanding of how to glean knowledge from data using modern tools, and there are numerous ways this can be achieved.

Edward Mills was speaking with Laura Elizabeth Lansdowne, Senior Science Writer for Technology Networks.