Quantitative Medicine Forum

Speaker:
Dr Wen Li
Biostatistician, Singapore Clinical Research Institute
Adjunct Assistant Professor, Centre for Quantitative Medicine
Office of Clinical Sciences, Duke-NUS Graduate Medical School

Host:
Dr. Benjamin Haaland
Assistant Professor, Centre for Quantitative Medicine
Office of Clinical Sciences, Duke-NUS Graduate Medical School

Date:
Wednesday, February 15, 2012

Time:
12.00pm to 1.00pm
(Lunch will be served from 11.30am)

Venue:
Amphitheater, Level 2
Duke-NUS Graduate Medical School
8 College Road, Singapore 169857
(opposite Singapore General Hospital, Block 6/7)

Contact Person:
Ms Megan Pooh, Duke-NUS Office of Clinical Sciences Program
Tel: 6601 1719 or Email: megan.pooh@duke-nus.edu.sg

Synopsis:
Surrogate markers can help identify patients who will have an early clinical benefit from a treatment, and herein are important not only for patients’ survival and quality of life but also for the cost of health care. When using a biomarker as a surrogate maker, the evaluation of the biomarker is inevitably necessary. However, owing to reasons such as biological variation and limited machine precision, the potential markers may be measured with large errors, which can make the surrogacy evaluation difficult. In this presentation, we will review the quantities used for the surrogacy evaluation, and introduce our method to deal with the measurement error in the surrogacy evaluation. Our method will be illustrated through
simulations and a case study using the real data from an osteoporosis research.

Biography:
Wen Li is a Biostatistician at Singapore Clinical Research Institute (SCRI), Singapore. She is also an Adjunct Assistant Professor with Centre for Quantitative Medicine at Duke‐NUS graduate Medical School, Singapore. Wen obtained her PhD degree in statistics at Iowa State University in US in 2009, and received her bachelor degree in applied mathematics from Peking University China in 2002. Wen had three years experience in clinical trials and more than ten years training/experience in statistics. Her research interests are in measurement errors and bio‐markers, dose‐response modeling, Bayesian modeling and inferences, time series, stochastic differential equations, and applied statistics in clinical trials.