External data continues to be a valuable resource in clinical trial planning. When used strategically, this information can help researchers save money, increase the effectiveness of their studies, and speed up the trial. But where does historical data enter the clinical trial discussion? There are multiple ways to tap into both internal and external data insights. Here’s how historical data is used in feasibility studies and trial implementation.
One of the most common ways to use historical data in clinical trials is to run feasibility studies. These are analyses of past trial data to understand what worked and what didn’t to assess what will likely work going forward. Researchers run feasibility studies on trials of similar subjects, treatments, patient pools and sizes.
“Most companies conduct at least some form of feasibility testing on their trial designs because they know that changes and miscalculations can cause delays and cost money,” write Alan Scott and Denis McMillan, senior director and vice president, respectively, at Parexel. “For example, the direct cost to implement a single protocol amendment averages approximately $500,000 in unplanned expenses and adds 61 days to project timelines.”
But how much can a feasibility study actually save researchers? And is it worth the cost? Ben Morgan, et al. at the U.K.’s National Institute for Health Research, analyzed whether feasibility studies really do help the trial process. Of the trials they surveyed, 20 were found to be unfeasible. By not conducting these trials, the organizations saved approximately 20 million pounds ($27 million) in research funding.
However, feasibility studies also slow the research process. The average trial with a feasibility study is 72 months, including 10 months to analyze the results of the study and 18 months to actually start the trial, Morgan, et al. report. Time is money.
“Feasibility studies are potentially useful at avoiding waste and de-risking funding investments of more expensive full trials, however there is a clear time delay and therefore some potential waste in the existing research pathway,” the researchers write.
Historical data plays a key role in feasibility analysis because it helps researchers identify problems in their trials. However, these studies can also slow down trials and prevent key treatments from getting to market.
More researchers are starting to tap into historical data as it becomes available. Digital publications and comparable data sets allow researchers to evaluate trials clearly and compare them to other sources.
“With greater availability of historical data and new methodology … the inclusion of historical data in clinical trials is an increasingly appealing approach for larger disease areas as well, as this can result in increased power and precision and can minimize the burden on patients in clinical trials,” write Jessica Lim, et. al, in Therapeutic Innovation and Regulatory Science. “However, sponsors must assess whether the potential biases incurred with this approach outweigh the benefits and discuss this trade-off with the regulatory agencies.”
There are multiple concerns when incorporating historical data into clinical trials — namely increasing the type 1 error rate. A type 1 error rate occurs when researchers report significant results when there are none (also known as a false positive). Fortunately, there are ways to account for this.
“It seems possible to improve the power and precision of the analysis of a clinical trial and simultaneously control the type I error rate, by including historical trials that are sufficiently comparable to the current trial,” write Joost van Rosmalen, et. al in an article for Statistical Methods in Medical Research. “To do so, it is necessary to use a method that accounts for between-trial heterogeneity … so that the historical data are included adaptively.”
As long as researchers are aware of the risks of evaluating historical data and clear on how they came to their conclusions, it’s possible to incorporate historical data from the start of the trial.
Feasibility is a highly generalized term in clinical trial research. It doesn’t immediately define what is feasible and a trial that is feasible in one aspect might not be in another. This is good news for researchers, as historical data can contribute to certain elements of feasibility testing, even if it can’t look at each element.
For example, feasibility testing can be used if you are looking to conduct studies in multiple regions globally. Additionally, historical data in feasibility testing can help you avoid the same mistakes as researchers before you. This applies to internal studies within your organization and external studies by peers at other research facilities.
Reviewing historical data can help you understand past trial design, including what worked and what didn’t. This analysis can also help your team understand which trials successfully recruited patients and how.
As more researchers use historical data for feasibility analyses, they are also looking for other ways to incorporate this third-party data.
“The randomized, controlled trials (RCTs) are still the golden standard, the study with historical control or external control can be used when concurrent controls are impractical or unethical,” says C.Q. Deng, vice president of biostatistics, clinical programming and data management at United Therapeutics. “Many drugs, biological products or medical devices have successfully been approved or cleared by regulatory agencies for marketing authorization using the evidence generated from the clinical trials with historical or external control.”
Historical controls use old data to control for new data that is reviewed by researchers. It is used as known information that is proven and factual against the results of the study.
“In general, one is searching for [historical controls] HC data that is as similar as possible to the patients being enrolled in the study of interest,” write Mercedeh Ghadessi, et. al, in the Orphanet Journal of Rare Diseases. “Data may come from different sources with a variety of structure and quality, which results in different biases and concerns for use of different types of HCs. This must be accounted for when a decision is made to use HCs in a clinical trial.”
This also comes with its own drawbacks. Just because data is published doesn’t mean it’s accurate or that researchers can make sound comparisons to their own data. This is why it’s so important to highlight where data insights come from and how they are used.
There are multiple ways to access historical data in clinical trials to evaluate your feasibility, while also decreasing the risk that these previous trials negatively impact your design. By aggregating data across several trials, you can decrease the risk that poor trial design or results from a former project affect how you view curated insights. Even if your research is primarily in a niche field, where there aren’t many studies, you can still pull data from similar research projects that were focused on similar types of illness, demographics, or treatments.
The challenge, for many researchers, is sourcing this historical data. Where can you find a large pool of reliable clinical trials that can be easily compared and contrasted on one platform? This is where Anju Software comes in.
Our team has developed TA Scan, a comprehensive tool that aggregates and analyzes clinically relevant public data. Our tool pulls from more than 400,000 unique clinical trials that take place across almost 200 countries. You never have to worry about clinical blind spots, because almost any study on any population is at your fingertips.
TA Scan is also updated weekly, which means new trials and discussions are constantly added. TA Scan includes data from 22 clinical trial registries around the world, pulling information from two million healthcare professionals, 6.5 million publications, and 630,000 presentations. The sheer volume of this information is staggering.
Not only can you access these studies – which make for an impressive library – but you can also tap into interactive reporting through advanced analytics. The AI technology developed by Anju allows researchers to compare and contrast studies as they develop their own trials. Instead of manually finding and comparing studies, this system can do it for you. The Trial Feasibility Wizard, in particular, is an integral tool for any researcher who wants to perform feasibility testing.
By making it easier to access historical data, TA Scan can help organizations gain better insights and build stronger, more effective clinical trials. This isn’t a substitute for traditional feasibility analysis, but rather a process for making it more efficient.
Clinical trial developers are experiencing a renaissance in data sharing. They have the ability to collect and review trials from across the globe. They can look to historical data for feasibility testing, trial development and results comparisons. However, like all advances in clinical trial technology, historical data needs to be used carefully and reasonably to ensure trials don’t overly rely on potentially unreliable insights.