PSI Webinar(s): Causal inference in Clinical Trials
Over the course of two sessions, a panel of 8 esteemed speakers will give an introduction to the topic, followed by a presentation of case studies & interactive panel discussion.
Date: Tuesday 27th June 2023
Time: 14:00-15:00 BST | 15:00-16:00 CET
Speakers: Weiliang Qiu and Cheng Wenren
Who is this event intended for? Statisticians in the Pharmaceutical Industry.
What is the benefit of attending? EC50, the concentration of a drug that induces a response halfway between the baseline and maximum, is a key quantity to evaluate drug potency. In this talk, attendees will hear from Weiliang and Cheng who will be presenting their investigations on EC50 estimation based on multi-donor dose-response data via different approaches.
Weiliang Qiu1, Cheng Wenren1, Tamara Slavnic2, Els Pattyn1, Luc Essermeant1
1Non-Clinical Efficacy & Safety, Early Development & Research, Biostatistics & Programming, Sanofi
2 IT&M Stats.
Dose–response relationships are important in assessing the efficacy and potency of drugs, which can usually be characterized by a 4-parameter logistic (4-PL) model: EC50, slope, lower asymptote, and upper asymptote. EC50, the concentration of a drug that induces a response halfway between the baseline and maximum, is a key quantity to evaluate drug potency. For multi-donor dose-response data, it is often the interest to estimate the overall EC50 and its 95% confidence interval (CI). A few multi-donor EC50 estimation methods have been proposed in literature. Jiang and Kopp-Schneider (2014) systematically compared meta-analysis and nonlinear mixed-effects approaches and concluded that meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments, especially suited in the case of small number of experiments, while nonlinear mixed-effects approach has issue of convergence failure probably due to overparameterization. In this talk, we investigated ways to improve nonlinear mixed-effects approach to alleviates its issue of convergence failure.
Weiliang Qiu, Els Pattyn, Cheng Wenren and Luc Essermeant are Sanofi employees and may hold shares and/or stock options in the company. Tamara Slavnic has nothing to disclose.
Weiliang Qiu is a Non-Clinical Efficacy and Safety statistician expert leader at Sanofi and is passionate about using statistics knowledge to help improve the lives of patients. He obtained PhD degree in Statistics from the University of British Columbia in 2004 and have worked at Brigham and Women's Hospital/Harvard Medical School for 14 years since 2004.
Weiliang joined Sanofi Non-Clinical Efficacy and Safety (NCES) team in 2018 and provided statistical supports for non-clinical studies in a variety of therapeutic areas, such as translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and Genome Medicine Unit. He also works with the NCES team to develop and implement innovative statistical methods to analyze the data from these studies.
Cheng Wenren is a Principal Statistician in the Non-Clinical Efficacy & Safety (NCES) team at Sanofi. Prior to joining Sanofi in 2021, Cheng worked as a CMC Statistician at Bristol-Myers Squibb. Cheng earned his PhD in Statistics from Bowling Green State University in 2014, where his thesis focused on "Mixed model selection based on the conceptual predictive statistic".