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Clinical Trials Could Yield Better Data with Fewer Patients Thanks to New Tool – ACRP

Following its discovery in 1799, the Rosetta stone, which features the same message in three different languages, became an invaluable tool for deciphering the previously untranslatable hieroglyphic style of Ancient Egyptian script because the other languages used on the stone were already known to linguists. Today, clinical researchers can take advantage of a new tool offering something akin to the Rosetta stone’s breakthrough gift—a way to take feedback from clinical trial participants that is often collected but left unused and translate it into actionable data. 

The University of Alberta researchers who developed the new statistical tool say it can help trial teams evaluate the results of studies, with the aim of allowing smaller trials to ask more complex research questions and get effective treatments to patients more quickly. They describe the “Chauhan Weighted Trajectory Analysis” (CWTA) in a paper in the journal BioMedInformatics, terming it an improvement on the Kaplan-Meier estimator, the standard tool since 1959. 

The Kaplan-Meier test limits researchers because it can only assess binary questions, such as whether patients survived or died on a treatment. It can’t include other factors (such as adverse drug reactions) or quality-of-life measures (such as being able to walk or care for yourself). The new tool allows simultaneous evaluation and visualization of multiple outcomes in one graph. 

“In general, diseases aren’t binary,” explains first author Karsh Chauhan, a fourth-year MD student at the University of Alberta, in a press release about the research. “Now we can capture the severity of diseases—whether they make patients sick, whether they put them in hospital, whether they lead to death—and we can capture both the rise and the fall of how patients do on different treatments.” 

John Mackey, a breast cancer medical oncologist and professor emeritus of oncology, added this tool allows researchers to do a smaller, less expensive, quicker trial with fewer patients, and get the overall benefit of a new treatment more rapidly out there in the world. Mackey and Chauhan began working on the statistical tool three years ago when they were designing a clinical trial for a new device to prevent bedsores, which affect many patients with long-term illness. They wanted to look at how the severity of illness changed during treatment, but the Kaplan-Meier test wasn’t going to help. 

“Dr. Mackey said to me, ‘If the tool doesn’t exist, then why don’t you build it yourself?’ That was very exciting,” says Chauhan, who also has a BSc in engineering physics, which he calls a degree in “problem-solving.” 

The researchers tell ACRP that their tool is applicable to patient outcomes generated from virtually any kind of trial design. “The CWTA can be performed on any trial dataset to compare fluctuating disease course over time between two populations,” they add. “Those data can be entered, two arms at a time (control and experimental), into the current software to generate the CWTA analysis graph and p-value as the test statistic. This visual representation of patient outcomes is very intuitive to understand and explain.” 

They say they will soon launch sample size calculators for the standard two-arm randomized controlled studies, so that in future, people who wish may design their studies with the CWTA as the primary endpoint. 

“Some additional functionality that we are working on is the ability to perform integrated covariate modelling (e.g., age, sex, race, stratification factors) and its effect on outcomes in a manner analogous to Cox regression,” they note. “For those, we recommend using conventional tools for now, and staying updated on subsequent papers and chauhanwta.com,” where a downloadable template providing an example of the CWTA’s formatting may be found. 

Soon, the researchers say they will have instructional content available on how to use the template and the tool, though they suspect clinical trial staff, investigators, and statisticians will find the approach intuitive and very simple to implement and explain to clinicians. 

The tool has been tested through retrospective analyses of multiple completed trials in oncology, and the researchers have published on two large cancer trials in metastatic breast cancer and melanoma, they also told ACRP. “We have also conducted 1,000-fold simulations of stochastically generated clinical trials to permit direct comparison to Kaplan-Meier survival analysis. These steps demonstrated that CWTA, by incorporating a full clinical dataset with two or more health state transitions, requires smaller sample sizes for equivalent power. It also reduces both Type I and II errors.” 

CWTA may also be used to analyze studies where the subjects enter with an established disease; it can capture and analyze both patient improvement and patient worsening as it plots a staircase that can either fall or rise. “We refined the software to visually capture and display a study where patients don’t necessarily start at extremes of a disease state, but somewhere in the middle,” the researchers explain. 

Further, in cancer trials, one of the key questions that plague investigators and those in drug development is “Do we power for progression-free survival, or overall survival?” By using CWTA, the researchers say that the answer is “We will capture both in one CWTA statistic and we only need half the patients,” because each patient provides two datapoints to the analysis. 

It will take time for regulatory bodies to learn about and understand the CWTA’s utility, Chauhan and Mackey add. “So, for registration path trials, we are not yet in a position to recommend CWTA as the primary endpoint,” they say. “However, for secondary endpoints, it is ideally suited to capture the multiple important endpoints that happen to a patient during their clinical trial participation (both efficacy, safety, and potentially combined efficacy/safety endpoints).” 

For non-registration trials such as investigator-initiated studies, the two-fold reduction in sample size requirements to power a study for a primary CWTA endpoint will be very attractive, the researchers say. “Eventually, we hope that CWTA will evolve to be accepted by regulatory bodies,” they note. “Furthermore, CWTA should markedly reduce numbers of required animals in preclinical protocols, which will be of interest to animal safety committees and research scientists.” 

Edited by Gary Cramer