BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Wednesday 26th January 2022\nTime: 14:00-15:00 GMT\nSpeak ers: Kevin Ding \;(AstraZeneca) \;and Juan Abellan \;(GSK)\n\n Who is this event intended for? \;Statisticians working on regulatory submissions\nWhat is the benefit of attending? \;To learn more about t he 'how' and 'why' of tipping point analysis\, using relevant examples fro m previous studies.\nRegistration\nYou can now register for this event. Re gistration fees are as follows:\n- \;Members of PSI \;= Free of ch arge\n- \;Non-Members of PSI \;= £\;20+VAT\nTo register for t he session\, please \;click here.\nOverview\nTo assess the strength of clinical study findings\, regulatory authorities often request tipping po int analyses. However what is a tipping point analysis\, and how is one pe rformed? What are the different approaches for continuous\, binary and tim e to event data? Kevin and Juan present some practical examples.\n\nTo vie w the flyer for this event\, please click here.\nSpeaker details\n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Abstract \n \n \n \n \n \n Juan Abellan\n \n \n Juan is a statisti cian by training\, by experience and by passion. He studied Maths and Stat s and did his PhD at the University of Valencia (Spain)\, and worked as a research fellow at Imperial College London. He has worked as a statisticia n in various roles in Public Offices\, Academia and Industry in Spain\, Ge rmany and the UK. He's currently a GSK fellow and Statistics Director at t he Statistics and Data Science Innovation Hub\, based in London. His curre nt main interests include the use of Bayesian thinking for better quantita tive decision making in drug development\, estimands and their estimation in the presence of missing data and the analysis of data collected with di gital wearable technologies.\n  \;\n \n \n Tipping point sensitivity analysis for time-to-event: a c ase study in belimumab\n In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-even t endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such chang es is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental\, control) pairs of post-censoring h azard rate changes form the TPA scenarios. Within each of the TPA scenario s\, participants who are censored are imputed a time to the event of inter est and are administratively censored if the imputed time exceeds the leng th of the follow-up. Results are combined across imputed datasets using Ru bin&rsquo\;s rules. Finally\, the plausibility of the scenarios where the results tip is discussed.\n \n \n \n \ n \n Kevin Ding\n \n \n Kevin has 13 years of industry experience of designing and implementi ng study design of clinical trials in the areas of both general medicine a nd oncology. Kevin previously worked in J&\;J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use o f estimand and approaches of dealing with missing data in late phase trial s. He presented &ldquo\;The Application of Tipping Point Analysis in Clini cal Trials&rdquo\; in 2018 JSM meeting.\n  \;\n \n \n Practical use of Tipping Point Analysis in reg ulatory submissions of clinical trials\n The tipping point anal ysis (TPA) approach has gained popularity recently as an approach for perf orming the sensitivity analysis under the missing not at random (MNAR) ass umption. This presentation will review why TPA gets popular in clinical tr ial submissions\, its implementation for binary endpoints for time-indepen dent imputation and time-dependent imputation\, general procedure of TPA i mplementation for continuous endpoints\, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that u se TPA as sensitivity analysis to illustrate the practical use of this met hod in real trials and important consideration points from the regulatory perspective.\n \n \n \n\n \;\n\n DTEND:20220126T150000Z DTSTAMP:20260913T052908Z DTSTART:20220126T140000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Webinar: Tipping Point Analyses - Introduction & Case Studies UID:RFCALITEM639248741481793911 X-ALT-DESC;FMTTYPE=text/html:Date: Wednesday 26th January 2022
\nTime: 14:00-15:00 GMT
\nSpeakers : Kevin Ding \;(AstraZeneca) \;and Juan Abellan& nbsp\;(GSK)
\n
\nWho is this event intended for ? \;Statisticians working on regulatory submissions
\nWhat is the benefit of attending? \;To learn more about the 'how' and 'why' of tipping point analysis\, using relevant examples f rom previous studies.\n

Registration

\n

You can now register for this event. Registration fees are as follows:
\n- \;Members of PS I \;= Free of charge
\n- \;Non-Members of PSI \;= £\ ;20+VAT
\nTo register for the session\, please \;click here.

\n

Overview

\n

To assess the strength of clinical study findings\, reg ulatory authorities often request tipping point analyses. However what is a tipping point analysis\, and how is one performed? What are the differen t approaches for continuous\, binary and time to event data? Kevin and Jua n present some practical examples.
\n
\nTo view the flyer for th is event\, please click here.

\n

Speaker details

\n\n
\n\n \n \n \n

Speaker

\n \n \n

Biography

\n \n \n

Abstract

\n \n \n \n \n

Juanedit
\n J uan Abellan

\n \n \n

Juan is a statistician by training\, b y experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain)\, and worked as a research fellow at I mperial College London. He has worked as a statistician in various roles i n Public Offices\, Academia and Industry in Spain\, Germany and the UK. He 's currently a GSK fellow and Statistics Director at the Statistics and Da ta Science Innovation Hub\, based in London. His current main interests in clude the use of Bayesian thinking for better quantitative decision making in drug development\, estimands and their estimation in the presence of m issing data and the analysis of data collected with digital wearable techn ologies.

\n

 \;

\n \n < td valign="top" style="width: 298px\;">\n

Tipping po int sensitivity analysis for time-to-event: a case study in belimumab

\n

In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random as sumption. The method is based on multiple imputation and it assumes the ha zard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for ea ch treatment arm. The (experimental\, control) pairs of post-censoring haz ard rate changes form the TPA scenarios. Within each of the TPA scenarios\ , participants who are censored are imputed a time to the event of interes t and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubi n&rsquo\;s rules. Finally\, the plausibility of the scenarios where the re sults tip is discussed.

\n \n \n \n \n


\n Kevin Ding

\n \n \n

Kevin has 1 3 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kev in previously worked in J&\;J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associat e Director for early phase oncology. Kevin has 14 academic journal publica tions and has special research interest in practical use of estimand and a pproaches of dealing with missing data in late phase trials. He presented &ldquo\;The Application of Tipping Point Analysis in Clinical Trials&rdquo \; in 2018 JSM meeting.

\n

 \;

\n \n \n

Practical use of Tipping Point Analysis in regulatory submissions of clinical trials

\n

The tipping point analysis (T PA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption . This presentation will review why TPA gets popular in clinical trial sub missions\, its implementation for binary endpoints for time-independent im putation and time-dependent imputation\, general procedure of TPA implemen tation for continuous endpoints\, and Interpretation of TPA result based o n clinical input. The presentation then shows six real examples of FDA sta tistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspec tive.

\n \n \n \n\n

&n bsp\;

\n

\n
END:VEVENT END:VCALENDAR