![]() ![]() Seeking solutions to problems of this nature, Baron has drawn on the theories of some of his- tory’s most famed statisticians: Carlo Bonferroni and Sture Holm, who advanced multiple testing theory as well as Albert Shiryaev, Abraham Wald, and Jacob Wolfowitz, who are considered the fathers of sequential analysis. In 2009, while attending a workshop on multiple comparison problems, Baron first questioned how multiple testing was studied in relation to sequentially collected data and discovered a diverse range of problems yet to be solved. While consulting on clinical trials, Baron realized that sequential analysis without a way to handle multiple tests concurrently would never be clinically useful. He began researching change-point analysis, which studies when a significant change has occurred in a dataset, while pursuing his PhD at the University of Maryland before connecting this topic to sequential analysis. ![]() By combining these methods, Baron is working on novel statistical approaches to detect significant changes, with huge implications across a range of fields.īaron joined the American University community in the fall of 2014, but has been working on sequential analysis and related applications since the beginning of his career. While fairly straightforward in isolation, these two methods applied together might decrease the average number of data points needed to make decisions or detect changes while intelligently controlling the allowed error rate. Multiple hypothesis testing is testing for significance across multiple tests concurrently. Sequential analysis is the concept of statistical estimation or deci- sion-making in real time as data is collected, as opposed to retrospectively on a fixed sample size, as is typically done. Professor Baron’s research is rooted in two statistical techniques: sequential analysis and multiple hypothesis testing. ![]() Lowering the cost of clinical trials may lead to lower cost of treatments, thus reducing the overall cost of health care. In the long term, this would mean more efficient use of resources and potentially life-saving reductions in the amount of time needed to establish a drug’s safety. But new, more efficient methods that push the boundaries of modern statistics could allow clinicians to determine with statistical certainty, based on fewer subjects, that a drug is safe. Such standards are determined (and limited) by currently available statistical methods. In order to be sure that a drug will not have an adverse effect on humans, certain statistical standards must be met in the drug’s clinical trials. More importantly, it could save participants from unknown risks. Lowering the number of subjects needed in a trial could save valuable time and resources. Currently, clinical trials are expensive, time consuming, and often require testing a large number of subjects in order to be sure that a drug is ready for human consumption. But for Professor Michael Baron, a recent addition to American University’s Department of Mathematics and Statistics, these priorities are just a part of his everyday research on sequential analysis and multiple hypothesis testing.Ĭonsider the cost of a clinical trial. Treating cancer and protecting the world against new terrorist threats might seem like a lot to tackle for the average person. ![]()
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