Room 300, School of Arts and Sciences
Central Campus
Ahmedabad University
In the first half of the talk, Girish Aras will introduce key ideas and methodologies of causal inference in the scientific foundations of evidence-based medicine. I will trace the history (20th century) of modern drug development through the evolution of the law known as The Food, Drugs and Cosmetic Act. The law was instituted and further evolved in response to some major global health events/crises. The US Congress gave sweeping powers to the FDA to interpret and execute the law to develop a drug approval process based on empirical data and science. We will also trace the law’s impact on global drug policies. In that context, we will discuss ‘adequate and well-controlled studies and the role of statistics in the process. We will discuss some of the important examples on the way.
Girish Aras has worked in academia, the federal government, and the Biotechnology/Pharma industry. He held leadership and supervisory positions in the government (Department of Biostatistics at the Center for Drug Development and Research, Food and Drug Administration) and in the pharma industry at Johnson & Johnson (a large pharma company), Amgen (a mid-size pharma company when he began there), and Esperion (a small biotech company). The initial years of his career (approximately ten years) were spent in academic positions, starting as a lecturer at Bombay University, followed by several years at the University of California, Santa Barbara, with a brief stint at IIT Bombay. He continued methodological research and publishing during his time in the government and industry as well. Currently, he offers statistical and regulatory consulting services to pharmaceutical companies, specialising in the strategic and secondary review of submission modules, DMC statistical membership, strategic reviews of protocols, and statistical analysis plans, mostly in dermatology, immunology, cardiovascular, and antiviral therapeutic areas. He is also an elected Fellow of the American Statistical Association.
