A Perspective on Clinical Statistics: Getting Beyond the Sizzle
Today’s blog comes to us from Georgette Asherman, founder of Direct Effects. She was one of the first writers in the early days of our blog, providing us with the article, “Sample Size Calculations: Thinking About Effect Size.” She has been a professional statistician in pharmaceuticals, consumer products, business services and public policy for over 10 years. She has been associated with organizations such as Unilever, Bristol-Myers Squibb, Chase Manhattan Credit Card Services, and the New Jersey Department of Health. In recent years she has developed an interest in quantitative aspects of modern biological sciences. She has worked in clinical and non-clinical biostatistics, chemistry data analysis and instrument capability studies.
She holds an M.S. in Statistics from Rutgers University and a B.A. from Cornell University. She is a member of the American Statistical Association and the New York Area SAS Users Group. Her contributed article and contact information follow.
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For many in businesses that rely on clinical research, the ‘sizzle’ of a headline or claim seems to be enough. But imagine the following scenario:
A prospectus sits on your desk. There is a sentence “The formulation shows a p-value of .023, significance at 95%, at a dose of 10 mg. Sounds good…its statistically significant…move on. But then you get a call six months later. A regulatory reviewer questions the randomization technique. But you thought everything was okay at 95% confidence. They did the statistics.
Statistics are the language for reporting results of efficacy and safety trials. We report significance, confidence and p-values, the probability of an event under certain conditions. These statements make their way into press releases and business documents.
In truth, statisticians play a role before the study is started. They advise on the study design, data collection and implementation. While the study is in progress, they bridge communication between the computer professionals such as programmers and data managers and the medical professionals such as clinical investigators and research associates.
In the jargon of the industry, the research on human subjects is referred to as Phase 1 through Phase 4 clinical while other activities such as drug discovery, manufacturing and toxicology get lumped into “non-clinical”. Clinical statisticians do work in a structure of tables, listings and graphs that adheres to the requirements of the FDA and various other national and international organizations. The roles vary for non-clinical statisticians. There is more interest in applications of statistics in early stage research and manufacturing processes.
One can find various definitions of the word ‘statistics,’ including the vernacular usage as any display of numbers. The American Statistical Association in its career section website (www.amstat.org/careers) answers the question “What is statistics?” as follows–Statistics is the scientific application of mathematical principles to the collection, analysis, and presentation of numerical data. Statisticians talk in the language of science-observation, estimation, hypotheses, generalization and modeling. Unlike other approaches to data analysis that aim only for a mathematical optimization, statistical analysis concerns itself with the scientific context of the data collection such as the measurement ranges and precision of lab equipment. It is not black-box computation.
In the early 20th century a mathematical framework for statistics emerged built on the backbone of probability theory, an older branch of mathematics. The expansion of this mathematical framework continues in universities around the world. While clinical research sometimes uses new techniques, it relies on basic ideas like distributions of averages and randomization. Randomization is a plan for treatment assignment that is independent of human choice. We know the probability that a subject is placed in a treatment group. This assignment lets the researcher use probability-based statistics to assess the effect of a treatment and whether it is just a result of ‘chance.’
As a professional statistician, most people find what I do mysterious. They remember a dreaded required course. If older they think of grayish books with grids and curves. If younger they think of the horrible software they were forced to use. Fortunately these classes, the books and the software are now a lot more applicable, even fun.
But you don’t have to take a new-fangled level 200 class to bring make clinical statistics more relevant. A good start is thinking about the numbers that express the result. This will make your questions more effective. A desired clinical change can be expressed as a change in a decimal number, a count, or a percent. A ‘20% drop’ can explain going from ‘200 to 160′ or ‘15 to 12′. This is where the ‘scientific context’ becomes important. The expression of a clinical result is the end-product of the clinical practices, the regulatory guidelines, previous company studies, and the judgment of the current statistician, and perhaps a previous statistician.
My ending comment is stay aware. A short phrase in a business or legal document is a small blip in the thousands of pages in a clinical trial submission. The minds of regulatory reviewers are at once specialized and focused but tuned into current trends. Clinical statistics can become pivotal in the approval process.
Georgette Asherman
Applied Statistician
Direct Effects, LLC.
www.directeffects.net
201 673-4301
