|A conversation with Dr. Joseph G. Ibrahim, director of the UNC Center for Innovative Clinical Trials|
The Center for Innovative Clinical Trials at the University of North Carolina
at Chapel Hill has been established to look
for better ways to design and conduct clinical trials.
The following is excerpted from an interview with center
director, Dr. Joseph G. Ibrahim, Alumni Distinguished Professor of
biostatistics in the UNC Gillings School of Global Public Health, and Director of the
Biostatistics Core at the Lineberger
Q: What do you hope
to accomplish with the Center?
Dr. Ibrahim: We
want to find ways to speed up and improve the clinical trial process, and make
it a more efficient, precise assessment of treatments, whether they are drugs,
medicines, devices or behavior modification programs. We want to find ways to get results
faster, but we want them to be more precise and useful in determining if the
treatment is effective, and if so, for which patients.
The ultimate goal is better healthcare.
Q: What can you do to improve the clinical trial process? Most of you are statisticians. You deal with math, not molecules. Can you find a mathematical cure for cancer or any other disease?
Well, we don't discover treatments,
but statisticians play an enormously important role in assessing whether
treatments work and for whom. The statistics - or the mathematics - are a
critical part of designing a trial and assessing the results to determine if
they are meaningful.
Q: What's different
about your approach?
Dr. Ibrahim: Most
trials right now are designed to answer two questions. Does the treatment work?
And is it toxic? We believe the questions are more complex, and there are statistical
approaches that can help us evaluate a broader spectrum of data in the same
amount of time.
For years, statisticians have provided technical and
practical results for clinical trials, but we believe we can make the trials
more precise and definitive if we work more closely with the investigators as part of a
scientific team when designing the trials, deciding what to look for and how to
formulate and assess endpoints and interpret results. We can help determine what the most meaningful
analyses of the data would be, including toxicity as well as overall benefit.
If one is scientifically and statistically sound with their design and analysis
plan, then the trials could potentially be shorter, involving fewer patients.
That would make them less expensive, which should be reflected in the cost of
the treatment to patients. It also should help get effective new treatments to
Q: At what phase in a new treatment's development do you see the Center getting involved?
Dr. Ibrahim: I believe we could start as early as the Phase I level (early clinical trials to assess appropriate dose levels, toxicity, and side effects) or even at the conceptual stage. In the Phase I setting, we would develop better statistical methods to determine the most tolerable dosage (MTD) and optimal dosing schedules, as well as a better assessment of side effects. This would make a difference when moving to Phase II and III trials.
Q: Do you think you could come up with methods that might prevent FDA recalls that we've seen with some other marketed medicines due to side effects?
Dr. Ibrahim: Yes! I think this is where we could make a real difference in clinical trials. Right now, trials are typically designed to pay special attention to one primary endpoint.
Phase III clinical trials require a
multi-dimensional approach where we look at several endpoints simultanously. We
believe this will make adverse events and toxicity results more apparent
earlier on in the trials. We also think that there is a need for clearer "stopping
rules" - points at which you stop a trial either because of toxicity - the drug
is hurting patients - or because the drug is so ineffective that it's unethical
to continue the treatment, or
potentially for reasons that the drug is so effective that it's unfair to
withhold the treatment from other patients in the trial.
Q: You are an expert
in Bayesian methods - explain what that is and how you think it will help make
safer treatments available faster.
There are two main paradigms for analyzing data, namely the frequentist and Bayesian
paradigms. In the frequentist paradigm, you, in a certain sense, "start over" each time in the design and
analysis of a new trial without using preconceived notions. That is, the
frequentist paradigm does not offer a natural or well accepted mechanism for
incorporating preconceived notions or previous data into the design and
analysis of a new trial. The frequentist
paradigm is the one that is most often used now, especially for designing
studies and analyzing clinical trial data in the Food and Drug Administration's
Among many of its features, Bayesian methods lend themselves
to easier understanding interpretation of results, they involve the
incorporation of information you know from past experience, they allow for
direct probability (chance) statements, such as the chance that one treatment
is more effective than another, and they are a more natural paradigm for making
predictions about future outcomes with certain treatments or therapies.
There's room for both types of paradigms, but one of the
most important features for me in my collaborations with non-statisticians is
that Bayesian designs and analyses lend themselves to much easier understanding
and interpretation of results. For example, with frequentist analyses, results are typically reported in terms of a number
- a p-value - which really can only be well understood by statisticians.
Bayesian analysis methods lend themselves better to graphics and visual
descriptions of results that are more easily understood by general audiences
Q: Does the Food and Drug Administration and/or National Institutes of Health recognize Bayesian methods?
Dr. Ibrahim: It's
an issue both the FDA and the NIH are grappling with. In general, the biggest
issue that FDA is trying to deal with regarding Bayesian methods is how to
incorporate preconceived notions, called
prior information in statistical terms, in the design and analysis of a trial.
For example, they're asking themselves if historical data should be considered
in the analysis of a new study, or if it would bias the results of a new study?
If you do use prior information, how
would you use it? Should it be discounted in some way so that it does not have
too much influence over the new results?
Right now, there's no conventional or gold standard use of prior
information or historical data in a
Bayesian analysis because of a concern that the patient populations in two
different studies might not be the same, or there were different ways of
administering the study that would lead to non-comparable results.
What we are hoping to do at the Center is get a consensus
among experts - find methods everyone can be comfortable with. We believe there
is a better way to do the statistical design and analysis of clinical trials,
and we believe we can be the catalyst for change.
Q: Will the Center coordinate studies?
Dr. Ibrahim: No.
We are not a coordinating center, but we will work closely with the excellent Collaborative Studies Coordinating
Center that is housed in
the UNC Gillings School of Global Public Health's biostatistics department.
Q: The center is to be interdisciplinary - what fields do you anticipate being involved?
Dr. Ibrahim: Right now, center members include faculty from the School of Public Health's departments of biostatistics, epidemiology and maternal and child health, as well as from the UNC School of Medicine and Lineberger Comprehensive Cancer Center.
We anticipate having faculty from many other disciplines, too, including nutrition, environmental health, health policy, disease prevention, economics, business, general medicine, oncology, dentistry, psychology, psychiatry, phychometrics, biomedical engineering, nursing, biology, chemistry, and others.We also expect to have affiliated members from other universities and from industry. We're right here in the Research Triangle Park, with a wealth of academic institutions, pharmaceutical companies and clinical research organizations. It's an exciting place to be, with lots of potential for positive change.
|Last updated September 06, 2007|