How does the Journal webinar work?
Journal webinars are held every few months and last about an hour. Journal papers are carefully selected from recent issues of the
Royal Statistical Society's journals by editorial board for their importance, relevance and/or use of cutting-edge methodology; and authors are invited to present their work and take questions from an audience who 'dial in' to access the webinar.
Two papers are selected from our journals and authors will be invited to present their papers (20 minutes) followed by discussion (25 minutes) for each paper. Attendees will dial into a teleconference call. Papers and slides will be available to download two weeks in advance or you can log into the conference system and follow the presentation live online. These sessions are open to members and non-members. No need to pre-register. Audio recordings will be available for download shortly after the session.
Questions on the paper or general queries can be emailed in advance of the session to email@example.com.
Web http://mci-group.globalmeet.com/psijournalclub. Phone: +44 (0) 330 336 6011 and Passcode: 497 034 0644 or connect audio from within GlobalMeet (‘Call my phone’ or ‘Call my computer’). Dial-in details for outside of the UK (PDF)
Wednesday 12 July 2017, 2.30pm BST
Once a year, the RSS and PSI hold a joint webinar and this year’s joint event will follow the theme of 'adaptive signature design'.
Joint webinars focus on one key paper from the PSI, one from the RSS Journal. One or more discussants are also invited to join.
RSS author & paper:
Zhiwei Zhang, 'Subgroup Selection in Adaptive Signature Designs of Confirmatory Clinical Trials', published February 2017 in JRSS Series C, Volume 2.
Co-authored by Meijuan Li, Min Lin, Guoxing Soon, Tom Greene and Changyu Shen.
Abstract: The increasing awareness of treatment effect heterogeneity has motivated flexible designs of confirmatory clinical trials that prospectively allow investigators to test for treatment efficacy for a subpopulation of patients in addition to the entire population. If a target subpopulation is not well characterized in the design stage, it can be developed at the end of a broad eligibility trial under an adaptive signature design. The paper proposes new procedures for subgroup selection and treatment effect estimation (for the selected subgroup) under an adaptive signature design. We first provide a simple and general characterization of the optimal subgroup that maximizes the power for demonstrating treatment efficacy or the expected gain based on a specified utility function. This characterization motivates a procedure for subgroup selection that involves prediction modelling, augmented inverse probability weighting and low dimensional maximization. A cross-validation procedure can be used to remove or reduce any resubstitution bias that may result from subgroup selection, and a bootstrap procedure can be used to make inference about the treatment effect in the subgroup selected. The approach proposed is evaluated in simulation studies and illustrated with real examples.
Dr Zhiwei Zhang is Associate Professor of Biostatistics at the University of California, Riverside. He received his PhD in Biostatistics from the University of Pittsburgh in 2004. Prior to his current position, he has worked at the US Food and Drug Administration (as Mathematical Statistician) and the US National Institutes of Health (as Investigator of Biostatistics). Dr. Zhang has done methodological research in many areas of biostatistics, including causal inference, precision medicine, and clinical trial design and analysis.
PSI author & paper
Gu Mi, 'Enhancement of the adaptive signature design for learning and confirming in a single pivotal trial' published May 2017 in Pharmaceutical Statistics.
Abstract: Because of the complexity of cancer biology, often the target pathway is not well understood at the time that phase III trials are initiated. A 2-stage trial design was previously proposed for identifying a subgroup of interest in a learn stage, on the basis of 1 or more baseline biomarkers, and then subsequently confirming it in a confirmation stage. In this article, we discuss some practical aspects of this type of design and describe an enhancement to this approach that can be built into the study randomization to increase the robustness of the evaluation. Furthermore, we show via simulation studies how the proportion of patients allocated to the learn stage versus the confirm stage impacts the power and provide recommendations.
Dr Gu Mi is a Research Scientist at Eli Lilly and Company in Indianapolis, Indiana, USA. He received his PhD in statistics from Oregon State University in June 2014. He has been a clinical and biomarker statistician at Lilly Oncology with hands-on experience of oncology trials. He has been actively involved in trial designs, statistical methodology development in oncology, biomarker data analyses, and regulatory activities. He has been serving as an expert reviewer for eight peer-reviewed journals covering topics such as biopharmaceutical statistics, clinical trials, and bioinformatics.
Webcasts, MP3s and slides from past events are available to download.