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Statisticians at St. Jude have developed a technique that allows researchers to statistically analyze results of clinical trials in which all participants receive the new treatment being studied and none is assigned to a control group that gets the existing treatment. Instead, the treatment group is compared with a so-called “historical control” composed of patients who got the existing treatment in a previous study.
A report on this new method appears in the August issue of Statistics in Medicine.
The St. Jude report is the first to describe a novel statistical method called sequential interim analysis using an historical control group. In an interim analysis, researchers statistically analyze 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 in order to determine if the trial can be stopped early.
“This technique lets investigators determine how probable it is that their decision to stop the trial would have changed if they had let the clinical trial continue to the end,” said the paper’s first author, Xiaoping Xiong, PhD, Biostatistics.
“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,” said co-author James Boyett, PhD, Biostatistics chair. “Now if they decide to stop the trial they can be confident they are making the right decision.”
The new technique is especially useful when results of preliminary studies strongly suggest that the treatment will be effective, and investigators do not want to deny it to people who could benefit by assigning them to the control group, according to Boyett.