Log in

Upcoming events

    • Fri, April 23, 2021
    • 09:00 - 10:00 (PDT)

    Fri, April 23, 2021, 09:00 – 10:00AM (PDT), 12:00 – 1:00PM (EST)


    According to a recent report, nearly 70% Phase II trials failed to move forward to Phase III. Part of the reasons caused such high failure rate could be inefficient trial design and trial monitoring. In this talk, we introduce the concept of dynamic data monitoring (DDM) for ongoing clinical trials. We develop the principles and procedures for dynamically monitoring on-going clinical trials and demonstrate that the accumulative treatment effect can be automatically estimated and continuously accessible over the information time. Building on the adaptive group sequential (AGS) methods, and by taking the advantage of modern eClinical technologies, we developed a clinical trial “radar” system on which the Wald statistics, conditional power, trend, timing for sample size re-estimation, alerting of early stopping for efficacy and/or futility can be automatically displayed. Thus, the trial can be intelligently monitored. Through simulation, we demonstrate the power of DDM in guiding a promising trial to success or detecting a “hopeless” trial to stop. We provide an example of DDM application in the 1st remdesivir trial in Wuhan China.


    Dr. Tai Xie received Ph.D. in Applied Math and Statistics from University of Arizona in 1993. Right after his Ph.D., he worked at the Arizona Cancer Center (AZCC) for three years. From 1996-2004, he worked for Pfizer for 5 years, JNJ for 2 years and Eli Lilly for 2 years holding various positions from Sr. Biostatistician to Associate Director. He founded Brightech International in 2004, a data focused CRO and acquired Shanghai Magnsoft Software Development Ltd (Magnsoft) in 2009, the first EDC company in China. Magnsoft was renamed as CIMS Global. He serves as CEO for both companies with over 90 employees in US and over 50 in China and Taiwan. He also served as CEO for Biopharm Solutions for 3 years, a biotech company focused on innovative drug delivery technologies. Dr. Xie is an Adjunct Assistant Professor of Biostatistics Department, School of Public Health at the Rutgers University. He has extensive experience in innovative trial design and statistical analysis and reporting, clinical data management, EDC, eClinical and eHealth. In recent years, Dr. Xie has been actively interacting with the regulatory authority and drug industry in China. He was invited several times by China's CFDA to participate in authoring the guidance on EDC and by influential professional organizations (such as China Heart Congress, CHC) to deliver courses and speeches on a variety of topics. Dr. Xie is also an active researcher with a number of research papers published in distinguished journals on various topics including cancer prevention, adaptive design, dynamic data monitoring, survival analysis, personalized medicines and more.

    Joe Shih Bio

    Dr. Joe Shih, Professor Emeritus of Biostatistics (July 1, 2019), and former Chair of Biostatistics Department (1999-2017), School of Public Health, and Director of Biometrics Division of the Cancer Institute of New Jersey (1999-2019), Rutgers University – the State University of New Jersey. Prior to joining academia, he spent 17+ years at Merck Research Laboratories.  He served in the US FDA’s Advisory Board for reviewing new drug applications.  He is a distinguished statistician and opinion leader in industry and academia.  He has collaborated extensively with physicians in various therapeutic areas and published over 160 papers in statistical methodology and medical research. His book “Statistical Design and Analysis of Clinical Trials” is a top-chosen graduate-level textbook for many universities.  His expertise includes adaptive designs and missing data issues in clinical trials, and evaluation of therapeutic interventions. He is an ASA (American Statistical Association, 1996) Fellow and elected Member of the International Statistical Association (ISI, 2001).

    Strategic Alliance:

Past events

Fri, March 19, 2021 IP Legal Considerations for Precision Medicine and Digital Health Companies
Fri, February 19, 2021 Machine Learning Based Integrative Multi-Omics Analysis— Presented by Global Champion of All Three PrecisionFDA AI Challenges
Fri, January 29, 2021 How to quantify and interpret the treatment effects for comparative clinical studies of COVID-19 diseases?
Fri, November 13, 2020 Telehealth and Other Advancements in Healthcare Technology Spurred by COVID-19
Fri, October 02, 2020 A New Measurements-Based Approach to Machine Learning for Bioinformatics
Fri, September 25, 2020 Under the hood of 1Point3Acres COVID-19 map & tracker
Fri, August 28, 2020 Demystifying the drop-outs in single cell RNA-seq data
Fri, July 17, 2020 Using multiple natural experimental designs to triangulate the impact of a policy change
Fri, May 29, 2020 How AI is changing the financial service industry
Fri, March 13, 2020 AI Drug Discovery and Repurposing
Fri, February 28, 2020 The i3+3 Design: A Rule-Based Dose-Finding Approach for Phase I Trials
Fri, December 13, 2019 Statistical Considerations in the Clinical Development of Novel Cancer Therapies
Fri, November 15, 2019 15 Tips on Making Better Use of R Markdown
Wed, October 02, 2019 Principles of Data Science
Fri, September 20, 2019 AI Derived Personalized Solution: The Key Driver in the Era of Digital Health
Fri, August 09, 2019 Scalable Automatic Machine Learning with H2O
Fri, June 07, 2019 Network Meta-Analysis for Decision-Making
Fri, May 03, 2019 Global Prediction of Gene Regulatory Landscape Using Bulk and Single-Cell RNA-seq
Fri, March 08, 2019 Innovative adaptive design for immune-oncology trials
Fri, February 22, 2019 Survival analysis methods for non-proportional hazards
Tue, January 08, 2019 Design of Dose-Response Clinical Trials
Fri, November 16, 2018 Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics
Fri, October 19, 2018 Machine Learning in Medical Imaging: the Challenge and Opportunity in Drug Development and Precision Medicine
Fri, September 14, 2018 Disease Heritability Inferred from Familial Relationships Reported in Medical Records
Fri, July 20, 2018 Big Data and Artificial Intelligence in Healthcare
Fri, May 25, 2018 The Genomic Landscape and Pharmacogenomic Interactions of Clock Genes in Cancer Chronotherapy.
Mon, May 21, 2018 Estimation of Treatment Effect under Non-Proportional Hazards
Mon, May 21, 2018 Subgroup Identification in Drug Development: Are Post-Selection Adjustments of Efficacy Useful?
Mon, April 30, 2018 An accurate and robust imputation method scImpute for single-cell RNA-seq data
Fri, March 30, 2018 Genome-scale signatures of gene interaction from compound screens predict clinical efficacy of targeted cancer therapies.
Thu, February 22, 2018 Expression Recovery in Single Cell RNA Sequencing
Wed, January 31, 2018 Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte
Thu, October 19, 2017 Statistical Challenges in Immuno-Oncology, a Cellular Therapies Perspective
Tue, September 26, 2017 Big-Data Analysis Points Toward a New Cancer Therapeutic Discovery Approach
Thu, September 14, 2017 Software Patenting: The Current State of the Law
Mon, August 21, 2017 The Asthma Mobile Health Study, a Large Scale Clinical Study Using ResearchKit

For more events please check:

Monthly Activity

选项 : 历史 : 反馈 : Donate 关闭

选项 : 历史 : 反馈 : Donate 关闭

@Dahshu 2020

选项 : 历史 : 反馈 : Donate 关闭
Powered by Wild Apricot Membership Software