Introduction to Biostatistics: Hypothesis Testing
Hypothesis testing is critical for the analysis of clinical trials, and its techniques have useful applications long after the completion of a trial. Whether you are starting a trial and need to formulate benchmarks, or setting a standard for measuring Adverse Event (AE) data throughout the life-cycle management phase, hypothesis testing is the foundational tool necessary to produce accurate, scientifically-sound data.
This interactive, 120-minute session will give you the tools to set-up your hypotheses, identify the correct test to use, complete the test, and make your decision on the hypothesis in question. Additionally, the course will aid you in turning your statistical data into an answer that will make sense in the “real world.” You will learn about a and b risks plus different types of tests, such as the z-test and t-test, and what a p-value is. Additionally, we will talk about how you use a p-value to make your decision. Furthermore, you will develop the necessary skills to employ these hypothesis testing techniques in your applications. Course content will be presented with pertinent examples including valid conclusions.
Come to the 120-minute presentation and you will be able to:
- Develop a foundational understanding of principles of hypothesis testing
- Use software to run the analysis and get pertinent information to make your decision based on the data (we will be using the free, open-source rcommander software)
- Understand which type of test should be used for a given situation
- Understand how to interpret the results
- Understand how to convert the statistical results into real-world conclusions
This course is ideal for life science personnel who need a foundational understanding of biostatistics to address every day responsibilities, and is ideal for new hires or those who need a refresher. Those in a clinical trials setting will find this course especially useful, although it is of benefit to all staff with upstream/downstream development responsibilities as well.
Robert Parody, PhD, is an Associate Professor in the Center for Quality and Applied Statistics at Rochester Institute of Technology. He has over 15 years’ experience working as a statistician improving processes and products. He has extensive experience in the pharmaceutical and medical device industries.
Dr. Parody is well versed in analyzing data, leading teams and mentoring persons in the use of statistical tools. He is especially skilled at utilizing statistics, including experimental design, sample size determination and statistical modeling techniques to develop and improve processes. He has created and delivered courses including online and industry seminars to all types of participants, especially busy professionals and adult learners.
Dr. Parody received his BS in Chemical Engineering from Clarkson University, his MS in Applied Statistics from Rochester Institute of Technology, and his PhD in Statistics from the University of South Carolina. He is a member of the Association of Clinical Research Professionals (ACRP) and the International Society of Pharmaceutical Engineers (ISPE).
Dr. Parody’s areas of expertise and interest include experimental design, quality risk management, pharmaceutical quality by design (QbD) and continuous improvement.