Page 39 - ISMCON souvenir 2021
P. 39

ISMSCON - 2021

                      Subgroup Identification of Targeted

               Therapy Effects on Biomarker for Time to

                                                   Event Data




                                 Gajendra K. Vishwakarma 1 and Atanu Bhattacharjee 2, 3
                                            1Department of Mathematics & Computing,
                                    Indian Institute of Technology (ISM), Dhanbad-826004, India
                                      2Section of Biostatistics,Centre for Cancer Epidemiology,
                                         Tata Memorial Centre, Navi Mumbai-410210,India
                                          3Homi Bhabha National Institute, Mumbai, India


             Abstract

             Background: The initiation of biomarker-driven trials has transformed the oncology drug development
             process. The approach with conventional drug development process (phase I, phase II, phase III) is being
             abandoned where first-in-human explored the doses and activity of different sites of cancer (“basket
             studies”). It questioned the basic frame-work about the drug development process. The ALK, ALK/ROS1
             and EGFR inhibitors trial for non-small cell lung cancer (NSCLC) are the best examples in this context.
             Now it is required to be expanded for all other sites as well.
             Objectives: To explore the dose response modeling on MTA and thereafter extends it towards time-to-
             event algorithm.
             Methods: Suppose a total of n patients are selected assigned for different doses. A dataset is prepared
             to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is
             measured through MTA. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the
             proposed algorithm. The analysis is carried out with a simulation study.
             Results: The work is proposed in the statistical methodology to support the biomarker-driven trial for
             oncology research.
             Conclusion: This subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian
             approach. This proposed method will be useful to decide the TLV of MTA from initial observation to
             predict the long-term survival. This method is suitable for personalized medicine and can be adopted for
             targeted therapy as well.

             Keywords: Bayesian algorithm, Biomarker, Personalized Medicine.


























             CONFERENCE SOUVENIR                                                                               37
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