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Date: Tuesday\, November 10\, 20
26 | 11:00 AM &ndash\; 12:30 PM EST
Location: Online
via Zoom
Jointly organized by: ASA BIOP Safety Work
ing Group\, PHUSE\, and PSI/EFSPI
Who is this event in tended for? This webinar is intended for statisticians\, safety s cientists\, physicians\, and other researchers involved in the planning an d interpretation of safety analyses in clinical trials. It is particularly relevant for those who want to deepen their understanding of safety estim ands and the methodological considerations in safety data analysis.
Benefit of attending: Understand key estimand considerati ons for single and aggregated safety analyses and learn from oncology expe rience on estimating adverse event (AE) incidence and assessing whether an AE represents an adverse drug reaction (ADR).
Th is ASA BIOP Safety Working Group webinar\, co-organized with PHUSE and PSI /EFSPI\, provides an overview of the ICH E9(R1) Estimand Framework and its relevance and application to the analysis of clinical trial safety data\, for both single studied and aggregated analysis across multiple studies.< /p>
Through presentations by industry experts\, attendees will gain insi ght into the use of the Estimand Framework for safety analyses. The webina r will explore how different clinical safety questions can be translated i nto appropriate estimands and aligned with corresponding estimators. Speak ers will also discuss practical considerations for defining and handling i ntercurrent events\, including competing risks and informative censoring\, and their implications for interpreting safety outcomes.
As the sec ond webinar in a planned series on analysis of safety information in clini cal trials\, this session will present examples from oncology and non-onco logy settings to illustrate current thinking and practical approaches to a pplying ICH E9(R1) principles in safety analyses.
\nTo register for thi s event\, please \;< a href="https://psi.glueup.com/event/maths-meets-medicine-exploring-career s-in-the-pharmaceutical-industry-130333">click here.
| Speaker\n < /td> | Biography\n | Presentation Abstract\n |
 George Kordzakhia\, Ph.D. \;Statistical Science Senior Director\, As traZeneca \; | Dr. George Kordzakhia is a Statistic al Science Senior Director in the Cardiovascular\, Renal and Metabolic (CV RM) Biometrics group at AstraZeneca in Gaithersburg\, USA. Before joining AstraZeneca in 2022\, he spent more than 15 years at the FDA\, working in the Center for Drug Evaluation and Research (CDER). There\, he led statist ical teams supporting multiple clinical review divisions and played a key role in the regulatory evaluation of new drug applications\, gaining an in -depth understanding of the statistical\, clinical\, and regulatory consid erations that shape the assessment of drug safety and benefit-risk. At AstraZeneca\, Dr. Kordzakhia provides statistical leadership and strate gic input to clinical development programs across the CVRM portfolio. He h as also contributed to several scientific publications on statistical safe ty analysis in clinical trials. Drawing on his experience on both the regu latory and industry sides\, he is committed to advancing rigorous\, transp arent\, and practical statistical approaches to safety evaluation. & nbsp\; | Safe ty Estimands for Single Studies and Aggregated Analysis Agg regated analyses across multiple clinical trials are crucial for comprehen sive safety evaluations. The aggregation of studies offers significant ben efits\, such as enhanced precision in estimations. However\, it demands a methodical approach to prevent the introduction of unnecessary biases in c omparative analyses and to ensure that estimates remain meaningful. In thi s presentation\, we employ the ICH estimand framework to guide the aggrega tion of clinical trials\, discussing essential principles and statistical considerations vital for planning integrated safety analyses. |
 Ellen Snyder\, Ph.D. | Dr. Ellen Sim Snyder has worked i n the Biostatistics Department at Merck &\; Co.\, Inc.\, Rahway\, New J ersey\, USA\, for more than 30 years. She spent more than 20 years in Late Development Statistics\, leading and supporting new drug applications acr oss multiple therapeutic areas\, and has led the Oncology Therapeutic Area in Clinical Safety Statistics for 11 years. Between completing her master &rsquo\;s and PhD degrees in Biostatistics at the University of North Caro lina at Chapel Hill\, she worked in Clinical Epidemiology at the Universit y of Pennsylvania School of Medicine. In this role\, she taught statistics to postdoctoral medical fellows in the International Clinical Epidemiolog y Network (INCLEN) and provided statistical support for large pharmacoepid emiologic and other epidemiologic studies. She has authored or coauthored more than 50 publications in the medical and statistical literature. She i s an active member of the ASA Biopharmaceutical Section Safety Working Gro up\, where she leads the Oncology Safety and Safety Estimands for Observat ional Studies task forces and serves on the Methodology and Leadership Tea ms. She recently joined the PHUSE Safety Analytics Team and APPRAISE\, a E uropean Union-funded pharmacovigilance project involving partners from the pharmaceutical industry\, academia\, and the European Medicines Agency.\n |
This presentation will examin e the estimation of adverse event (AE) incidence from clinical trial data and analyses to assess whether an AE represents an adverse drug reaction ( ADR)\, with an emphasis on oncology studies. It will highlight the applica tion of the ICH E9(R1) Estimand Framework to define the safety question\, select aligned estimators\, and report estimates at clinically meaningful time points\, in addition to conventional AE tabulations. Various intercur rent-event strategies for addressing treatment switching or discontinuatio n\, censoring\, and competing events that lead to unequal exposure times\, will be examined from a safety perspective. The discussion will explain w hy cumulative incidence estimates based on a while-on-treatment strategy f or selected intercurrent events may be misleading when interpreted as AE r isk over a common period or as evidence for or against a causal relationsh ip with the drug. The presentation will also consider Section 10.7 of ICH M11 in relation to planning safety analyses\, including risk estimation an d comparison. |
 Wei Wang\, Ph.D. | Dr. Wei Wang is an Associate Dir ector of Biostatistics at Daiichi Sankyo\, where he serves as a cross-prog ram lead safety statistician supporting the clinical development of oncolo gy compounds. Prior to joining Daiichi Sankyo\, he worked at Merck as a sa fety statistician supporting multiple oncology development programs. His r esearch interests include statistical methods for safety monitoring in ong oing clinical trials\, causal inference\, and survival analysis. He is an active contributor to the ASA Biopharmaceutical Section Safety Working Gro up. Wei received his PhD in Biostatistics from Rutgers University.< /p> |
This presentation focuses on a simulation study comparing common estimators of adverse event (AE) incidence&mdash\;the crude proportion\, Kaplan&ndash\;M eier (KM) estimate\, and Aalen&ndash\;Johansen (AJ) estimate of cumulative incidence&mdash\;across several data scenarios with and without censoring and competing events. The results illustrate how censoring and competing events affect the estimators and how each estimator addresses a different clinical question. \; \; |
 Viviana Rodriguez\, Ph.D. | Dr. Viviana Rodriguez is an Associate Principal Scientist in Biostatistics and Research Decision Sciences (BARDS)\, Clinical Safety Statistics\, at Merck &\; Co.\, Inc . Rahway\, New Jersey\, USA. A Fulbright Scholar\, she received her Ph.D. in Biostatistics from Virginia Commonwealth University in 2020. She provid es statistical leadership for oncology safety programs and contributes to strategic BARDS initiatives supporting drug development and safety evaluat ion. Before joining Merck\, she spent 15 years in academia as an Assistant Professor\, leading and collaborating on applied clinical research across multiple disciplines. Her professional interests include generalized stat istical modeling\, survival analysis\, compositional data analysis\, and d rug safety.\n | Estimating &\; Comparing the Incidence of Adverse Ev ents using Clinical Trial Data Focusing on Oncology Trials: \; A Safet y Estimand Perspective (Part 3 of 3) Kaplan&ndash\;Meier (KM ) and Aalen&ndash\;Johansen (AJ) estimators rely on the assumption of noni nformative censoring\, which may be questionable when treatment discontinu ation is associated with subsequent adverse event (AE) risk. This presenta tion will examine analyses for assessing departures from this assumption a nd introduce inverse probability of censoring weighting (IPCW) as an appro ach to addressing potentially informative censoring. An overview of IPCW w ill explain how weights are calculated using the inverse of each participa nt&rsquo\;s estimated conditional probability of remaining uncensored\, wi th the probabilities and resulting weights updated over time during follow -up. \; |
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