CROs to benefit from new trial analysis technique
services may benefit from a new technique that allows researchers
to statistically analyse results of clinical trials.
The new method allows researchers to make a new treatment available to everyone in the study while still adhering closely to the gold standard of clinical study designs - the prospective randomised controlled trial (RCT).
Statisticians at St. Jude Children's Research Hospital have described this novel statistical method called sequential interim analysis using a historical control group.
In an interim analysis, researchers statistically analyse the accumulating results of the clinical trial at several points during the course of the study, rather than wait until the end of the trial to determine if the trial should be stopped early.
"It's not always possible to do a standard RCT when there are a limited number of patients available to participate, or when patients do very poorly on the standard treatment that the new treatment is intended to replace," said Xiaoping Xiong, associate member of the St. Jude Department of Biostatistics and the paper's first author.
"In such cases, the best option is to design a trial that allows all participants to get the new treatment and use previously treated patients as a historical control group," he added.
The explosion in the use of clinical trial services has even taken the industry by surprise as tightening of legislation as well as cost and time considerations means these services are rapidly gaining in popularity.
With pharmaceutical and biotechnology companies being the main customers, CROs are constantly striving to increase efficiencies in data capture, management and analysis as well as speed up the manual and cumbersome aspect of clinical trials.
Until now, there were no statistically valid methods that included interim analysis in the design of clinical trials that used historical controls, said the paper's co-author, James Boyett, chair of the St. Jude Department of Biostatistics.
"This technique also relieves investigators of the uncertainty they would otherwise have if they stopped a clinical trial before its planned end point, because their interim analysis tells them either that the treatment works or it doesn't," Boyett said.
Xiong's technique lets investigators determine the probability that their decision to stop the trial would have changed if they had let the clinical trial continue to the end.
"Investigators are ethically obligated to cease recruiting additional patients to the clinical trial as soon as there is statistical evidence that it is an improvement over - or is inferior to - the new treatment, compared to the historical control group," Boyett said.
"Now, if they decide to stop the trial they can be confident they are making the right decision."
He added that the new technique is especially useful when results of preliminary studies suggest that the treatment will be effective and when investigators do not want to deny that treatment to people who could benefit from it.
