Dr Daniel Sargent is the President of the Society of Clinical Trials and the primary consulting statistician for the Gastrointestinal Cancer Program of the Mayo Clinic Cancer Center, a non-profit worldwide organisation in medical care, research and education for people from all walks of life.
His primary research interests are in the area of the conduct and the methodology of clinical trials in cancer. Areas of active research include clinical trial design, design and analysis of studies involving tumour markers, meta-analyses, and survival analysis.
Statistics Views talks to Dr Sargent about the objectives and plans of the Society for Clinical Trials and his own career.
1. As President of the Society for Clinical Trials, what are your thoughts as you look back on the past year? What were your main priorities/ objectives, especially with regards to 2013 having been the International Year of Statistics?
The Society for Clinical Trials (SCT) is an international, professional organisation dedicated to the theory and practice of clinical trials. We have approximately 1000 members, sponsor the journal Clinical Trials, hold annual meetings that are attended by approximately 600 delegates, and pursue a wide range of outreach activities. By all of these metrics, 2013 was a very successful year for the SCT. Membership grew, our meeting was very successful, and, in a very major step forward for the society, we co-sponsored with the FDA our first Fall workshop ‘The SCT/FDA Benefit-Risk Workshop: Bridging Qualitative and Quantitative Assessments’, December 5 – 6, 2013 in Rockville, Maryland. This inaugural event was a great success, attracting over 150 attendees.
2. What are the Society’s plans for 2014 and what do you hope to achieve?
In 2013 the SCT underwent a strategic planning process. Based on this effort, we believe our mission is best advanced through the support, engagement, and professional advancement of our members. In 2014 we are focusing on implementing key aspects of the SCT Strategic Plan in the following ways:
• Increase educational opportunities both within SCT and through partnerships with other organisations. Also, increase access to these opportunities through the use of technology
• Expand the Society’s role in contributing to important policy debates, in which our collective expertise in clinical trials has high relevance
• Sustain and continue to build a robust membership that represents varied disciplines and expertise within the science and practice of clinical trials
• Provide networking opportunities to support the professional development of our multidisciplinary membership
3. You have said that the Society differs ‘from groups specializing in one discipline, disease, or therapeutic area because we recognize the need for understanding and communications at all levels. What we share is a common desire to learn, while advancing the field of clinical research.’ How do you think the SCT has evolved during your time here overall and adapted to the changing needs of the community?
We have members that are physicians, data managers, regulatory specialists, IT professionals, statisticians, and other interested researchers. Due to this diverse set of specialties, we are constantly evolving our priorities to align with those of the clinical trials field. In recent years we have seen increasing emphasis on managing the regulatory burdens associated with clinical trials, on adaptive designs, on Data Safety Monitoring Boards, and on areas such as clinical trials endpoints, just to name a few. In all of these areas, statisticians play a key role, but none of these topics exists in a vacuum.
4. What are the advantages that a statistician can make use of by joining the SCT?
Medical research breakthroughs require an army of diverse specialists working together. Statisticians are an absolutely critical cog in the wheel. The SCT exposes statisticians to pressing methodological challenges that require the application and development of new statistical methods. Similarly to most societies, networking is a critical benefit – in this case networking with those outside of the specific discipline.
5. Please could you describe the validation procedure to us? How would you perform the validation for data sets?
High quality, reliable data is absolutely critical to clinical trials, and is an area that often in my opinion receives inadequate attention from statisticians. I personally am very reluctant to work on an analysis of a dataset unless I understand very clearly how the data was generated, what quality control procedures were used, whether it was collected in an unbiased manner (such as was there a placebo control group), and who had access to the data at all times. Data validation is a multiple step process and requires both clinical and statistical input. Statisticians may well not know if a data item is plausible or not from a medical point of view, but statistics can detect patterns in the data that case-by-case medical review may miss. Risk-based monitoring (where emphasis is placed on validating the data items most likely to be in error or those of greatest importance to the study endpoints) is a potentially major advance that has been gaining momentum and has the potential to streamline clinical trials. This has been a major focus of the Clinical Trials Transformation Initiative (http://www.ctti-clinicaltrials.org/), an organization that the SCT is a member of.
Medical research breakthroughs require an army of diverse specialists working together. Statisticians are an absolutely critical cog in the wheel. The SCT exposes statisticians to pressing methodological challenges that require the application and development of new statistical methods.
6. There has been recent debate that clinical trials are improving in terms of efficiency thanks to Bayesian analysis. Simply Statistics have said that ‘the basic principle underlying these ideas is the same: can we run a trial more efficiently while achieving reasonable estimates of effect sizes and uncertainties?’ Would you be in agreement?
As a Society, and me personally, we are agnostic about specific methods, as long as they work! I do strongly believe that both Bayesian and frequentist statisticians are making important advances in methodology to run trials more efficiently – we have no choice in this. For example, in the area of my medical collaboration (oncology) there are so many new agents to test, and patient populations are being so finely divided based on increased biological knowledge, that standard trial paradigms are not only impractical but in some cases unethical.
7. With an educational background in mathematics and then biostatistics from the University of Minnesota, when and how did you first become aware of statistics as a discipline and what was it that inspired you to switch from mathematics to biostatistics for you MS and then PhD?
As I expect is the case for many, wise mentorship helped guide my path. I actively sought advice from almost everyone I could find – professors, guidance counsellors, friends of the family that were in scientific areas, and others. I pursued internships of different flavours, seeking to find what truly interested me for a career. My first introduction to statistics was a 3 week summer course while I was still in high school, but I did not consider it as a career until discussions with through my undergraduate math advisor whose wife happened to be a statistician – a connection was made, which quickly bloomed.
8. You are the primary consulting statistician for the Gastrointestinal Cancer Program of the Mayo Clinic Cancer Center, and as such you are involved in multiple ongoing clinical trials of both cancer treatment and cancer screening. The research that you are a part of directly results in an increased understanding of tumour or patient factors that make tumours more or less aggressive. How does statistics play a role in the Center?
Statistics is integrated into almost all research going on within the Mayo Clinic Cancer Center. We have statistical faculty collaborating with every cancer center program, and are heavily involved in all major funded grants. Statisticians are voting members on the concept and protocol review committees, the data safety monitoring board, and other relevant committees. Statistical input is vital for every new protocol. The Biostatistics Shared Resource, as we are officially titled, is one of the largest and most widely used shared resources within the Center.
9. You have been responsible for launching some clinical trial databases. Please could you tell us more about these databases and their goals?
Every clinic trial is designed to address a single question. But if we can gather the data from many clinical trials, and put the data together, we can ask and answer so many more questions. Many questions related to subgroups of patients that don’t have enough patients in any one trial to answer that question.
The very first pooled database we assembled was to answer a question about the value of the treatment for elderly patients with colon cancer, and elderly patients are relatively rare in clinical trials. So we had to gather data from several trials to get enough elderly patients, in this case, patients over the age of 70, to determine the efficacy. After that, the next question was what about trial end-points; do we have to wait for overall survival as our primary trial end-point? Our goal was to determine whether we could identify an end-point earlier in the patients’ course of disease that would provide reliable information. In order to do that, we used those several trials from the elderly analysis, and added additional trials to create the ACCENT database, which included 18 trials, which allowed us to look at the relationship between a surrogate endpoint, in this case, disease-free survival, with overall survival, which was the true endpoint. We were able to demonstrate the disease-free survival was an accurate predictor of overall survival, across these 18 trials, and that led to FDA acceptance of disease-free as survival as an endpoint for stage III colon cancer. This endpoint allows new treatments to get to patients 2 years more quickly than they would have in the past.
Once we had that success, other people became aware of our database, started asking many more questions, and giving us more data. Now we have conducted at least a dozen analyses, some looking at very young patients, for example, patients under the age of 40, who are very rare for colon cancer. We’ve looked at the effect of race, white Caucasians vs. African American. We’ve looked at many such questions in patients with stage II or III colon cancer.
Due to this success, we partnered with Dr. Aimery deGramont to create another collaboration to gather data from stage IV colon cancer patients. We are just launching now a collaboration to collect data in pancreas cancer patients. And we are midway through a similar collaboration in follicular lymphoma, where we now have data from 15 trials and over 5000 patients.
There are two principles for using the databases: when someone gives us data, we never use the data for anything that has not been pre-agreed upon, and that makes people trust the process. The second principle is that any person who provides data can propose a new question to be answered by the database, but each time all the people who gave data have the opportunity to agree to let their data be used for the analysis or not. This means that contributors have no risk to give us data, because we never do anything with it that people who provided the data would object to. This has allowed our database to grow very large, and has allowed investigators all around the world to propose new questions that could never be answered any other way. So it has been very successful so far.
10. Over the years, how has your teaching, consulting, and research motivated and influenced each other? Do you continue to get research ideas from statistics and incorporate your ideas into your teaching?
Absolutely. All of my statistical research ideas are drawn directly from the medical research projects I am involved in. In the end, what is the value of new ideas if you cannot translate them into practice? I teach medical students the fundamentals of clinical trials and biostatistics, but also have the opportunity to travel, lecture, and give short courses to statisticians on new methods, which I find highly rewarding.
Contributors have no risk to give us data, because we never do anything with it that people who provided the data would object to. This has allowed our database to grow very large, and has allowed investigators all around the world to propose new questions that could never be answered any other way. So it has been very successful so far.
11. What has been the most exciting development that you have worked on in biostatistics during your career?
There have been many, but to single out one, it my work with the ongoing International Duration Evaluation of Adjuvant Therapy in Colon Cancer, or IDEA for short. IDEA is a first of its kind prospective planned pooled analysis that will merge data from 6 ongoing trials world-wide testing the non-inferiority of 3 months versus 6 months of therapy. To properly test this hypothesis, over 10,500 patients were required, which was larger than any single country could do alone. Building on my prior work with pooling data from completed trials, we formed the international IDEA collaboration to do the same thing with trials before they even started. The power (and admittedly the complexity) of such an undertaking is enormous, but in late 2013, we crossed the 10,500 patient goal.
12. What do you think the most important recent developments in the field have been? What do you think will be the most exciting and productive areas of research in biostatistics during the next few years?
Adaptive design, either Bayesian or frequentist, has been very hot lately, and I expect it will stay that way for reasons discussed previously. Methods for high-dimensional data are also in great need, as the dimensionality and thus complexity of datasets that we work with seems to have no bounds.
13. What do you see as the greatest challenges facing the profession of biostatistics in the coming years?
These are very exciting but also challenging times. Funding levels for research are decreasing; particularly concerning is that it can be very difficult for junior faculty to get a foot-hold in the field. Continued strong mentorship from senior statisticians is critical to help recruit and retain talent. As a discipline, the size and complexity of data that we are faced with draws attention from many other fields such as computer science and bioinformatics. This is a wonderful opportunity for collaboration, at the same time we must remember that many of these individuals in these fields do not have statistically based training in issues such as uncertainty and bias, which can (and do) easily creep into large datasets.
14. Are there people or events that have been influential in your career?
I have been incredibly blessed with mentors throughout my career. The individuals are too numerous to mention but essential input has come from Dr. Don Berry, who literally ran into me on the street on the University of Minnesota campus and started me on my journey to statistics, my PhD thesis advisor Dr. Jim Hodges at the University of Minnesota who provided the perfect mix of freedom, encouragement, and guidance at critical moments, and my first (temporary, post-doc only) boss Dr. Jim Neaton, also at the U of M who shaped my views on clinical trials. Final thanks go to my first ‘real’ bosses – Drs. Judith and Michael O’Fallon at the Mayo Clinic who created a wonderful environment for statisticians to prosper in both methodology and consultative research.
Copyright: Photograph appears courtesy of Dr Sargent