Lisa LaVange, Ph.D., Director of CDER’s Office of Biostatistics shares her perspective on a statistician’s role in CDER and on some of the work her more than 175 staff members take on every day.
Statisticians on both sides of the application are similar
Statisticians on both sides of the application process often have a dubious reputation of throwing cold water on any excitement.
I think there is a perception that scientists working in the pharmaceutical industry are motivated by a drug’s ability to generate revenue. However, during my time working in industry, I found that the real excitement was about developing a cure for a disease – in getting a drug to market that would make people feel better. But, this excitement can sometimes introduce prejudice into the evaluation of a drug’s potential. Therefore, one of the industry statistician's jobs is to maintain an objective eye to help determine that the results are sturdy and a drug is worth pursuing.
It may be surprising to some to know that statisticians within CDER sometimes play a similar role. CDER statisticians must maintain objectivity in order to allow the data that CDER receives to tell us about what a drug can and cannot do. We need to find potential problems in a drug before it gets onto the market, but we also need to make sure the problems are real, so that safe and effective drugs can get to market.
What sets a statistician apart
A statistician’s objectivity comes in large part from our training in logic, probability theory, and the application of sound statistical methods to data analysis. Years ago, when I was training, statisticians were uniquely focused on the data. We were the data managers, the data analysts, and the translators explaining what the data meant to non-statisticians. Now, people from other disciplines are often involved in data manipulation, data mining, and data exploration. This can be a good thing because important information is more likely to be found when more people with diverse backgrounds are looking at the data. A downside to this abundance of both “big data” and data analysts is that the proliferation of analyses and results can make separating true signals from noise more difficult.
There are some data or results that statisticians might focus on a little differently than other analysts. Our heightened awareness of uncertainty and ability to appropriately quantify that uncertainty also set us apart. To me, statistics is not just using the tools; it’s a way of thinking, a way of approaching things, and a way of interpreting results.
Roles of CDER statisticians
I think statisticians have a very strong role to play in CDER. The three main roles of statisticians in CDER’s Office of Biostatistics (OB) are analogous to the three phases of the drug lifecycle: reviewing sponsor protocols during drug development, analyzing the data from clinical trials submitted to FDA for review, and assisting with post-market surveillance.
The review of statistical protocols is our bread and butter. These are usually protocols or study designs under an investigational new drug (IND) application where we advise on the viability of the study for addressing the hypothesis, whether it be demonstrating safety, finding the right dose, or proving effectiveness. We also review designs of post-marketing trials or meta-analyses of multiple trials to investigate a suspected safety signal.
OB’s statisticians review study designs for statistical soundness and to uncover the potential for bias, giving sponsors the opportunity to address these flaws prior to the start of the study. We don’t want patients participating in studies that have no chance for success due to design flaws. So, the ultimate goal of our protocol reviews is for sponsors to design the best possible study so that the data are trustworthy and the results will not raise more questions than they answer.
Once the clinical studies are finished and the sponsor files a New Drug Application (NDA) or biologic license application (BLA), OB’s statisticians become data analysts. At this stage, we check the datasets to be sure they are of high quality and provide enough evidence to support the application. After a thorough data review, we validate the sponsor's analyses and conduct additional analyses to verify any assumptions made about the data and about the relationships among variables. If questions arise about the validity of any assumptions required for key analyses, OB’s statisticians perform additional analyses to determine how sensitive the results are to variations in those assumptions.
In the post-market phase of the drug life cycle, OB’s statisticians use statistical surveillance methods to continue monitoring safety as the drug becomes used more broadly. For example, increasing reports to FDA’s spontaneous adverse event reporting system around a particular drug may prompt us to examine that signal further. If signal detection methods applied to these reports indicate the relationship between drug and adverse event is probably not due to chance, we may then move to evaluating data from post-market trials, observational studies, or health care claims to investigate the drug further. In some cases, we will undertake the task of combining data from all clinical trials of all drugs in a class and then analyze the pooled data using appropriate statistical methods, such as meta-analyses, to see if there is a safety signal across the drug class. The outcome of these analyses may be a regulatory action, like a labeling change, or in extreme cases, removing a drug from the market.
In Part 2, Dr. LaVange discusses the roles CDER statisticians play outside of reviewing drug applications, challenges they are facing, and opportunities for innovative statistical solutions.
Dr. LaVange joined FDA in September of 2011 as director, Office of Biostatistics in the Office of Translational Sciences. She received a B.A. in Mathematics from the University of North Carolina at Chapel Hill (UNC), a M.A. in Mathematics from the University of Massachusetts Amherst, and a Ph.D. in Biostatistics from UNC. Dr. LaVange’s early career was spent doing research at the non-profit RTI International, followed by ten years in the pharmaceutical industry. Immediately before joining FDA, she was a professor and Director of the Collaborative Studies Coordinating Center in the Department of Biostatistics at UNC.
- Clinical Data Interchange Standards Consortium
- ICH E9 Statistical Principles for Clinical Trials
- Article by Dr. LaVange in TIRS