SID VENKATARAMAN Statistics has become a permanent part of society. The application of statistics is seen on TV, in the media, and in magazines. As future researchers, health care providers, and educated individuals, it is imperative that we learn how to interpret research results and determine their reliability.
Suppose a pharmaceutical representative approaches a medical doctor to advertise the company’s drug and claim that in a clinical trial of 200 patients, 110 patients survived (as opposed to 90 survivors without the drug). Should the drug still be administered even though the survival rate does not change dramatically after taking the drug (45% to 55%)?
Without statistical analysis, this is a difficult question to answer. Did more patients survive due to the administration of the drug or was the improvement due to pure chance? Statistics helps us by providing a systematic method by which we can draw meaningful conclusions from obtained data.
As a future health care provider, you may not be the one conducting the statistical analysis, but it is likely that you will encounter statistics while reading research articles or discussing drug trials. When these situations occur, having a statistics background will enable you to be more of an asset to your team.
Dr. Viswanathan, a Biostatistics professor at UT Austin, echoed this sentiment on the first day of class. As someone who has worked heavily in industry, prepared statistical sections of Phase II and III protocols, analyzed plans and clinical study reports, and contributed to the preparation of submissions to the FDA for approval of clinical drugs, she has seen first-hand the benefits of working with doctors who are familiar with statistical analysis. It takes coordination between the physician and biometrician to run a successful drug trial, and the physicians with more familiarity with statistics can communicate more effectively with the biometrician.
To these ends, I highly recommend taking Biostatistics, SDS 328M, for anyone that is interested in going into the health professions or computational biology. The class is structured similarly to introductory statistics from high school with the addition of ANOVA testing, a common statistical test used to analyze differences between more than two group means. For example, this test can be used to understand differences in average blood pressure among different age groups. In this instance, an ANOVA test could be used to examine whether average blood pressure in each group is significantly different from the others.
Furthermore, Biostatistics teaches the basics of using R, a free software programming language for statistical computation. R is used widely in industry and research institutions for data mining and data analysis. Also, if you are interested in pursuing computational biology research on campus, knowing how to use R is a great advantage.
Over the last decade, the importance of physician familiarity with statistical analysis has increased dramatically. There has been a gradual move away from calculus and towards statistics as the mathematics requirement for medical schools, further substantiating the value of a statistical background in medicine. Fortunately, biostatistics is offered at UT every semester and it is taught by very dedicated professors, providing students with a solid statistical foundation and an introduction to analytical tools.