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.
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 \n | \n Biography \n | \n Abstract \n |
\n
| \n Juan is a statis tician by training\, by experience and by passion. He studied Maths and St ats 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 statistic ian in various roles in Public Offices\, Academia and Industry in Spain\, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub\, based in London. His cur rent main interests include the use of Bayesian thinking for better quanti tative decision making in drug development\, estimands and their estimatio n in the presence of missing data and the analysis of data collected with digital wearable technologies. \n\; \n | \n
Tipping point sensitivity analysis for time-to-event: a case s tudy in belimumab \nIn this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a t ime-to-event endpoint to assess the robustness of results to the censo ring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values r eflecting such changes is considered to vary the hazard rate post-censorin g independently for each treatment arm. The (experimental\, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios\, participants who are censored are imputed a time t o the event of interest and are administratively censored if the imputed t ime exceeds the length of the follow-up. Results are combined across imput ed datasets using Rubin&rsquo\;s rules. Finally\, the plausibility of the scenarios where the results tip is discussed. \n |
\n
| \n Kevin has 13 years of industry experience of designi ng and implementing study design of clinical trials in the areas of both g eneral medicine and oncology. Kevin previously worked in J&\;J (late ph ase oncology) and Novartis (late phase immunology) and now is working in A Z as Statistical Science Associate Director for early phase oncology. Kevi n has 14 academic journal publications and has special research interest i n practical use of estimand and approaches 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 Practical use of Tipping Point Analy sis in regulatory submissions of clinical trials \nThe tipping point analysis (TPA) approach has gained popularity recent ly as an approach for performing the sensitivity analysis under the missin g not at random (MNAR) assumption. This presentation will review why TPA g ets popular in clinical trial submissions\, its implementation for binary endpoints for time-independent imputation and time-dependent imputation\, general procedure of TPA implementation for continuous endpoints\, and Int erpretation of TPA result based on clinical input. The presentation then s hows six real examples of FDA statistical review of submitted NDA/BLAs (al l in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration p oints from the regulatory perspective. \n |
\;
\n\n