HD Lighthouse Contributing Editor's Comment: A revolutionary new statistical method could allow all study participants to take the therapeutic agents themselves - no one would have to take a placebo.
The words "exciting" and "statistics" are rarely heard in the same sentence. But new biostatistics research by Dr. Xiaoping Xiong and his team may fundamentally change the way clinical trials are done, especially for orphan diseases like Huntington's. The new approach could allow all study participants to benefit from a new treatment, with no one relegated to a concurrent control group. So the method can accelerate medical research in general, and its special characteristics could provide more conclusive results to boot.
Based on 15 years of work, Dr. Xiong and his colleagues at St. Jude Children's Research Hospital and the University of Maryland Greenebaum Cancer Center have developed a new statistical 'sequential procedure' for monitoring clinical trials (see abstract below). It allows enrolling all patients in a research trial in the experimental group, with no need to establish the usual concurrent control group for comparison. (A common form of this control group, used when little is known, as in HD, is the placebo group.) Using the new approach, the experimental group is compared instead at various intervals throughout the study to an historical control group, consisting of patients who received some 'standard treatment' in a previous study.
At each point of comparison, experimental group data are compared to all of the historical control group data. This iterative analysis allows an early decision on whether the trial is showing positive results. Researchers using the new technique can pinpoint earlier that the findings up to that point would almost surely be the same if the entire original planned trial were carried out. So trials can often be concluded faster than planned.
The new analytic technique could help support exciting new studies in Huntington's disease and other conditions with small patient populations. When patient populations are small, as in HD, it can be hard to enroll enough subjects to divide them into statistically meaningful experimental and standard control groups. Using the new technique, statistically rigorous conclusions can be drawn even when no concurrent but only historical control group data are available.
The value of the new approach hits home in considering all the promising agents for HD - creatine, fish oil, CoQ10, trehalose, selective serotonin reuptake inhibitors (SSRIs), and others that are already available - without even considering those still to come. These available agents might be tested much faster and more conclusively using statistical techniques like those of Xiong and his team. Up until now, the practical constraints on testing so many agents in such a small patient population have been overwhelming.
The new approach is especially useful when the experimental treatment is believed to be better than the traditional treatment, because all patients can benefit from the new therapy. In this regard, too, the new method is especially apt for Huntington's, since there are numerous promising, readily available, relatively nontoxic agents still to be tested for efficacy.
At the same time, the new monitoring procedure allows the trial to be cut off reasonably quickly if the new treatment is not panning out.
The Xiong team approach is based on a statistical technique known as the sequential conditional probability ratio test (SCPRT). What is novel about the approach is its application to the experimental paradigm described above, a feat made possible by employing a mathematical model of Brownian motion. (Brownian motion is the minute random movement of particles in or floating on the surface of a fluid. Mathematical models of this motion are sometimes used in statistics to describe the behavior of random variables.)
The approach could be especially useful in combination trials, which are strongly needed in HD research. For example, using the new technique, the historical control group could consist of patients who have taken creatine, while the active experimental group could consist of those taking both creatine and fish oil.
Also important, all the advantages of the new approach mean that research can be accelerated as a whole. Studies can be done more easily and cheaply without a concurrent control group. Research results can be obtained faster using the new forms of statistical comparison to an historical group, provided the latter meets some basic requirements. All this may propel research in a critical area like HD.
When clinical trials are well conducted, Dr. Xiong and his colleagues say, the new statistical technique should be more rigorous than any other form of clinical trial, except for the RCT [the randomized clinical trial, the gold standard]. The RCT is best for dealing with larger patient populations and therapeutics whose value is not as clear, when larger numbers are needed to obtain valid results. The new technique is enormously promising, therefore, for circumstances in which RCTs are difficult to perform, as for orphan diseases like HD.
For the full press release from St. Jude's, see:
http://www.stjude.org/media/0,2561,453_2816_21507,00.html
For the full original research report, see:
http://www3.interscience.wiley.com/cgi-bin/abstract/112732033/ABSTRACT?CRETRY=1&SRETRY=0
-- Ann Covalt, M.A. and Malcolm Casale, Ph.D.
Posted to the HDL: 15 Oct 2006

St. Jude first to describe new statistical method

Memphis, Tennessee, August 31, 2006

Analytical tool allows new treatment to be made available to everyone in a study; validates use of interim analyses to compare a new treatment group to a “historical” control group of patients
Statisticians at St. Jude Children’s Research Hospital have developed a new technique that allows researchers to statistically analyze results of clinical trials. In it, all participants receive a new treatment and none are assigned to a control group getting the existing treatment. Instead, the treatment group is compared with a so-called “historical control” composed of patients who received 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 this novel statistical method called sequential interim analysis using a historical control group, the authors said. 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 to determine if the trial should be stopped early.
The new statistical tool 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 randomized controlled trial (RCT), according to Xiaoping Xiong, Ph.D., associate member of the St. Jude Department of Biostatistics and the paper’s first author. In an RCT, participants are randomly assigned to either the group that receives the new treatment or the group that does not.
“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,” Xiong said. “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.”
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, Ph.D., 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.
Specifically, 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.
“This is a novel advantage of Dr. Xiong’s technique,” Boyett said. “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. Now, if they decide to stop the trial they can be confident they are making the right decision.”
According to Boyett, 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.
The researchers developed the new statistical technique in response to an opportunity in a clinical trial (SJMB96) already underway at St. Jude to study the treatment of children newly diagnosed with a brain tumor called medulloblastoma. Children in this study received the standard treatment of high-dose chemotherapy and irradiation, both of which can result in hearing loss.
Three years after SJMB96 began, the investigators wanted to study the use of a drug called amifostine, which previous studies suggested might protect children from hearing loss caused by the existing treatment. But they wanted the study to be designed so they could stop it early if statistical evidence indicated that amifostine either did or did not protect the children from hearing loss. “The problem was that there were not enough patients to put in a randomized control group and answer the question in a reasonable length of time,” Xiong said.
However, the same researchers were following exactly the same procedures using identical treatment for brain cancer with only the addition of amifostine. “Therefore, it was appropriate to use historical controls for this modified version of SJMB96; specifically, the children who had been already been treated on SJMB96. The researchers will now be able to determine whether the addition of amifostine to the treatment is beneficial,” Xiong said.
The other author of this study is Ming Tan, who was on the faculty at St. Jude and is currently Director of Biostatistics for the Greenbaum Cancer Center at the University of Maryland, Baltimore.
This work was supported in part by the National Institutes of Health and ALSAC.
St. Jude Children's Research Hospital
St. Jude Children's Research Hospital is internationally recognized for its pioneering work in finding cures and saving children with cancer and other catastrophic diseases. Founded by late entertainer Danny Thomas and based in Memphis, Tenn., St. Jude freely shares its discoveries with scientific and medical communities around the world. No family ever pays for treatments not covered by insurance, and families without insurance are never asked to pay. St. Jude is financially supported by ALSAC, its fund-raising organization.

http://www.stjude.org/media/0,2561,453_2816_21507,00.html