17th March 2018

Personalized Medicine: Statistical and Programming Challenges


Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person. It is a key topic in clinical trials in today’s and tomorrow’s world. Leading large pharma companies are coming up with ways to discover and develop medicines to foster disease prevention and come out with better outcomes for patients. From a data analysis standpoint, it is new for many of us in terms of data captured, its volume and analyzing it.



Personalized Medicine: Statistical and Programming Challenges and Standards – Sravan Nagi Reddy, PPD

"One Size Fits All!" for Precisioned/Personalized Drugs – Thought Journey – Divya Gunasekar, Novo Nordisk

Personalized Medicine: Clinical Trial Design Considerations and Statistical Aspect – Apoorva Singh, Quanticate

Machine Learning Techniques to Personalize Medicine – Vinodkumar Katikala, GSK

Personalized Medicine: Promises and Challenges – Pritish Dash & Gaurab Chakraborty, Chiltern

Genomically Driven Cancer Trials: An Evolution of Personalized Medicine – Kameshwari Peri, Syneos Health

Semi-parametric Bayesian Model for Personalized Medicine – Ayan Das Gupta, Novartis

Digital Disruption: A boon or Curse for Personalized Medicine and Q&A – Mayank Anand, Syneos Health, Gaurab Chakraborty, Chiltern & Arjun Roy, ABS Paris


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