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SUMMARY:Leveraging External Data for Testing Experimental Therapies with B
 iomarker Interactions in Randomized Clinical Trials - Professor Lorenzo Tr
 ippa\, Harvard T.H. Chan School of Public Health and Dana-Farber Cancer In
 stitute
DTSTART:20260227T130000Z
DTEND:20260227T140000Z
UID:TALK244327@talks.cam.ac.uk
CONTACT:Paul Browne
DESCRIPTION:All are invited to the hybrid Bradford Hill Seminar:\n\nLevera
 ging External Data for Testing Experimental Therapies with Biomarker Inter
 actions in Randomized Clinical Trials\n\nProfessor Lorenzo Trippa\, Harvar
 d T.H. Chan School of Public Health and Dana-Farber Cancer Institute\n\nRe
 gister to attend\nPlease note this will be a hybrid event.\n\nAttend in pe
 rson at: Large Downstairs Teaching Room\, East Forvie Building\, Forvie Si
 te\, Robinson Way\, Cambridge CB2 0SR.\n\nRegister to attend online at: ht
 tps://cam-ac-uk.zoom.us/meeting/register/f9LMsIBDQwqxwpWM0Fr9Hw#/registrat
 ion\n\n\nAbstract\nIn oncology the efficacy of novel therapeutics often di
 ffers across patient subgroups\, and these variations are difficult to pre
 dict during the initial phases of the drug development process. The relati
 on between the power of randomized clinical trials and heterogeneous treat
 ment effects has been discussed by several authors. In particular\, false 
 negative results are likely to occur when the treatment effects concentrat
 e in a subpopulation but the study design did not account for potential he
 terogeneous treatment effects. The use of external data from completed cli
 nical studies and electronic health records has the potential to improve d
 ecision-making throughout the development of new therapeutics\, from early
 -stage trials to registration.\n\nHere we discuss the use of external data
  to evaluate experimental treatments with potential heterogeneous treatmen
 t effects. We introduce a permutation procedure to test\, at the completio
 n of a randomized clinical trial\, the null hypothesis that the experiment
 al therapy does not improve the primary outcomes in any subpopulation. The
  permutation test leverages the available external data to increase power.
  Also\, the procedure controls the false positive rate at the desired 𝛼
 -level without restrictive assumptions on the external data\, for example\
 , in scenarios with unmeasured confounders\, different pre-treatment patie
 nt profiles in the trial population compared to the external data\, and ot
 her discrepancies between the trial and the external data. We illustrate t
 hat the permutation test is optimal according to an interpretable criteria
  and discuss examples based on asymptotic results and simulations\, follow
 ed by a retrospective analysis of individual patient-level data from a col
 lection of glioblastoma clinical trials.\n\nAbout Professor Trippa\nLorenz
 o Trippa\, PhD\, is a professor of biostatistics at Harvard T.H. Chan Scho
 ol of Public Health and Dana-Farber Cancer Institute. His research focuses
  on statistical methods for clinical trial design\, with an emphasis on Ba
 yesian and adaptive approaches\, external controls\, and precision oncolog
 y. Trippa’s work aims to improve the efficiency and reliability of trial
 s in complex settings\, particularly in cancer research\, and has connects
  methodological research and applied clinical studies.\n\nAbout the Bradfo
 rd Hill seminars\nThe Bradford Hill seminar series is the principal series
  of The Cambridge Population Health Sciences Partnership\, in collaboratio
 n with the PHG Foundation. This comprises the Departments of Public Health
  & Primary Care\, MRC Biostatistics Unit and MRC Epidemiology Unit at the 
 University of Cambridge\, bringing together a multi-disciplinary partnersh
 ip of academics and public health professionals. The Bradford Hill seminar
  programme of internationally recognised speakers covers topics of broad i
 nterest to our public health research community. It aims to transcend as w
 ell as connect the activities of our individual partners.
LOCATION:Large Downstairs Teaching Room\, East Forvie Building\, Forvie Si
 te Robinson Way Cambridge CB2 0SR.
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