Founder John P. A. Ioannidis, M.D., D.Sc., is the C.F. Rehnborg professor in disease prevention, professor of medicine, professor of health research and policy, and professor (by courtesy) of Statistics at Stanford. It is his life’s mission to assess favoritisms, replication, and reliability of research findings in biomedicine and other fields. In 2005, he published an article with the debated titled “Why Most Published Research Findings Are False,” which discussed “study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field.”
He concludes with the impossible of adapting to a gold standard in research, as there is no way to eliminate all elements of bias, chance, sample size so on and so on. However one of the most significant conclusions he came to was the need for “better powered evidence, e.g., large studies or low-bias meta-analyses.”
“Adaptive design is defined as a multistage study design that uses accumulating data to decide how to modify aspects of the study without undermining the validity and integrity of the trial,” Vladimir Dragalin, Ph.D., senior vice president of software development at Reston, Va.-based contract research organization Aptiv Solutions Inc. recently told Technology Networks. “By validity, we mean the minimization of statistical bias by using the correct statistical methods—and by integrity, we mean the minimization of operational bias through the use of appropriate trial execution technologies and working procedures, including the use of operational firewalls and independent data monitoring committees.”
In 2008, Howard S. Hochster M.D. published an article in Gastrointestinal Cancer Research titled “The Power of ‘P’: On Overpowered Clinical Trials and ‘Positive’
“When you set up a clinical study you set it up with a sig number of patients depending what type of product it is in order to make sure you guarantee meeting the requirements of the FDA or a European Union Notified Body (NB),” explained Bernard Sweeney, senior vice president of medical devices for Reston, Va.-based contract research organization Aptiv Solutions Inc., to Medical Product Outsourcing. “Consequently you select a higher number of subjects, and depending on the probability, reduce it slightly. If you use an adaptive design, you have the ability after a period to analyze the different arms of the group, and if you’re finding it’s been very positive and the products has been performing well, better than expected against the existing standard of care, then you have the ability to reduce the number of patients particularly in the standard of care, and apply to the FDA or NB much earlier for approval. Conversely, if its not working, you have a greater ability to stop the trial. It gives you the chance to readjust the number of patients to suit the outcomes whilst you’re in it and therefore under most situations reduce the number of patients needing to be treated.”
ACTs could easily be called “right-sized”
Sweeney told MPO that medical products are becoming more and more intricate both in terms of diseases treated and etiologies, and entering larger markets. The FDA is encouraging the move towards ACTs in order to limit the amount of patients in trials, but also because ACTs allow researchers to focus on small subgroups within the trial and figure out why a treatment may not be working or is working in ways otherwise expected.
“Let’s say a trial has 85 patients. You’ve got to wait until those 85 patients are on board and have been treated,” Sweeney continued. “But if you use an adaptive design, you have the ability of evaluating let’s say after 60 patients. Depending on those outcomes you may well find that the last 20-25 patients are not necessary. Or you may find that if you do something differently, you will have to increase patient numbers. But its all about optimizing patient numbers which is why the FDA is so encouraging of ACTs. With device trials, you are very likely to be able to reduce the number of patients, because most devices are basically engineered so you can predict early on more likely the outcome than with something that is a therapeutic agent for example.”
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