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Position Summary Mentored by experienced statisticians, Interns will work closely with senior-level statisticians on statistical methodology and/or application topics related to the design and analysis of clinical trials and/or nonclinical research on a variety of therapeutic areas. Tasks for this internship include literature review, development of innovative statistical methods and/or computational tools, data analysis and simulations to demonstrate utility of research, project work presentation to internal and external stakeholders, deliver R/SAS programs for implementation of the recommended statistical approach, and a summary report. The candidate will become more familiar with the drug development process with regards to study designs and study conduct, as well as regulatory requirements. The Student Intern will beneficiate from BMS working environment and be fully integrated in the Boudry Biostatistics group. Internship should be considered part of the student education and the work contribute partly/fully to his/her studies. Key Responsibilities The Student Intern will have to deliver an internship report, in alignment with requirements from his/her university. For BMS internal use, the student will develop a PowerPoint presentation of his/her work and findings, and a possible guidance document. At Bristol Myers Squibb, we are inspired by a single vision – transforming patients’ lives through science. In oncology, hematology, cell therapy, immunology, cardiovascular disease, and neuroscience – and one of the most diverse and promising pipelines in the industry – each of our passionate colleagues contribute to innovations that drive meaningful change. We bring a human touch to every treatment we pioneer. Join us and make a difference. Position Summary Mentored by experienced statisticians, Interns will work closely with senior-level statisticians on statistical methodology and/or application topics related to the design and analysis of clinical trials and/or nonclinical research on a variety of therapeutic areas. Tasks for this internship include literature review, development of innovative statistical methods and/or computational tools, data analysis and simulations to demonstrate utility of research, project work presentation to internal and external stakeholders, deliver R/SAS programs for implementation of the recommended statistical approach, and a summary report. The candidate will become more familiar with the drug development process with regards to study designs and study conduct, as well as regulatory requirements. The Student Intern will beneficiate from BMS working environment and be fully integrated in the Boudry Biostatistics group. Internship should be considered part of the student education and the work contribute partly/fully to his/her studies. Research project New assets triggered new challenges regarding the clinical development: multiple drug combinations to be explored, multiple schedules, multiple dose levels… Treatment selection designs is one of the best leads to improve the clinical development plan, by comparing, in parallel, multiple experimental treatment to a common SoC control group. Bayesian Adaptive randomization is a suitable method to allocate more patients over time to the treatment arms with the best early efficacy/safety outcomes (see Lixia Yi’s internship 2022). However, Type I error control is difficult to achieve through Bayesian Adaptive randomization. Pr Wason published in 2017 a new design (Drop-the-losers) which is a special case of adaptive randomization design. At each interim analysis, the worst experimental treatments gets a 0% randomization probability, while the best ones carry on with balanced randomization probabilities. This simplification allows one to guarantee good operating characteristics of the study design, including a strong control of Type I error (only normal endpoint explored in the publication). A strong control of Type I error makes this design very convenient for a Phase II/III study, as an inferentially seamless design. Another feature of this design is that, thanks to predefined numbers of selected treatment arms (after each interim analysis), the overall sample size is fixed. A counter-intuitive characteristic for an adaptive design, but very convenient when discussing the study’s timeline and resources. Illustration of a Drop-the-losers design: This design is labelled 4:2:1 (4 experimental treatments at Stage 1, 2 remaining at Stage 2 and only 1 left for the final analysis). The sample sizes per arm and per stage are predefined, leading to a fixed overall sample size . A more commonly used approach is the p-value combination (Jaki 2013 & Bauer 1999). However, this approach is usually described for a single efficacy analysis. Extending the p-value combination approach based on the Group Sequential Design method and related alpha-spending functions may provide another good option to deal with treatment-selection during a Phase 2/3 study. Illustration of combination test: Final dataset is split into two independent subsets, using a population flag (patients being randomized before vs after dose-optimization) Primary analysis is performed on each subset separately (from which p-values are derived and ) Then the combined p-value can be derived as follows (Inverse normal test): , with and being the relative information provided by each subset. is finally compared to the -level to be spent on primary endpoint. This -level can be derived from a spending function (similarly to Group Sequential Design approach). The objective of the internship will be to: Drop-the-losers design, extend the methodology to binary and survival endpoints, extend the methodology to add futility stopping rules, P-value combination Extend the methodology to Group Sequential Design assess final estimator’s bias (a well-known issue for most of adaptive designs), compare the design’s characteristics with other approaches (adaptive randomization, parallel studies using GSD) through a simulation study. Wason J, Stallard N, Bowden J, Jennison C. A multi-stage drop-the-losers design for multi-arm clinical trials. Statistical Methods in Medical Research. 2017;26(1):508-524. doi:10.1177/0962280214550759 Jaki, T. and Magirr, D. (2013), Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all promising treatments. Statist. Med., 32: 1150-1163. https://doi.org/10.1002/sim.5669 Bauer, P. and Kieser, M. (1999), Combining different phases in the development of medical treatments within a single trial. Statist. Med., 18: 1833-1848. https://doi.org/10.1002/(SICI)1097-0258(19990730)18:14<1833::AID-SIM221>3.0.CO;2-3 Key Responsibilities The Student Intern will have to deliver an internship report, in alignment with requirements from his/her university. For BMS internal use, the student will develop a PowerPoint presentation of his/her work and findings, and a possible guidance document. Internship outcomes may lead to publications and further research. Qualifications & Experience Internship is 4-6 months, as part of the university. Offered to Master or PhD student in Statistics (Applied Mathematics/Statistics/Biostatistics). Required skills: Adaptive designs Multiple testing procedures Optional: Survival analysis Bayesian statistics (used for adaptive randomization) Good working knowledge of using R/SAS or other mathematical software and MS Office products (Word, Excel, PowerPoint). Ability to work both collaboratively as part of a team and independently. Good verbal communication skills, ability to write clearly and effectively, in English language. Strong interpersonal skills with professional attitude. Owning, or able to obtain, permit to work in Switzerland. Must not be employed at the time the internship starts. Why You Should Apply Around the world, we are passionate about making an impact on the lives of patients with serious diseases. Empowered to apply our individual talents and diverse perspectives in an inclusive culture, our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Internship outcomes may lead to publications and further research. Qualifications & Experience Internship is 4-6 months, as part of the university. Offered to Master or PhD student in Statistics (Applied Mathematics/Statistics/Biostatistics). Good working knowledge of using R/SAS or other mathematical software and MS Office products (Word, Excel, PowerPoint). Ability to work both collaboratively as part of a team and independently. Good verbal communication skills, ability to write clearly and effectively, in English language. Strong interpersonal skills with professional attitude. Owning, or able to obtain, permit to work in Switzerland. Must not be employed at the time the internship starts. Mentored by experienced statisticians, Interns will work closely with senior-level statisticians on statistical methodology and/or application topics related to the design and analysis of clinical trials and/or nonclinical research on a variety of therapeutic areas. Tasks for this internship include literature review, development of innovative statistical methods and/or computational tools, data analysis and simulations to demonstrate utility of research, project work presentation to internal and external stakeholders, deliver R/SAS programs for implementation of the recommended statistical approach, and a summary report. The candidate will become more familiar with the drug development process with regards to study designs and study conduct, as well as regulatory requirements. The Student Intern will beneficiate from BMS working environment and be fully integrated in the Boudry Biostatistics group. Internship should be considered part of the student education and the work contribute partly/fully to his/her studies. Research project New assets triggered new challenges regarding the clinical development: multiple drug combinations to be explored, multiple schedules, multiple dose levels… Treatment selection designs is one of the best leads to improve the clinical development plan, by comparing, in parallel, multiple experimental treatment to a common SoC control group. Bayesian Adaptive randomization is a suitable method to allocate more patients over time to the treatment arms with the best early efficacy/safety outcomes. However, Type I error control is difficult to achieve through Bayesian Adaptive randomization. Pr Wason published in 2017 a new design (Drop-the-losers) which is a special case of adaptive randomization design. At each interim analysis, the worst experimental treatments gets a 0% randomization probability, while the best ones carry on with balanced randomization probabilities. This simplification allows one to guarantee good operating characteristics of the study design, including a strong control of Type I error (only normal endpoint explored in the publication). A strong control of Type I error makes this design very convenient for a Phase II/III study, as an inferentially seamless design. Another feature of this design is that, thanks to predefined numbers of selected treatment arms (after each interim analysis), the overall sample size is fixed. A counter-intuitive characteristic for an adaptive design, but very convenient when discussing the study’s timeline and resources. Illustration of a Drop-the-losers design: Let’s consider a design labelled 4:2:1 (4 experimental treatments at Stage 1, 2 remaining at Stage 2 and only 1 left for the final analysis). The sample sizes per arm and per stage are predefined, leading to a fixed overall sample size . The design’s algorithm leading to treatment selection and final drug comparison is applied as follows: Patients are randomized to 5 different arms (1 control + 5 experimental) At 1st interim analysis, the two worst experimental arms are dropped. More patients are randomized to the remaining arms (including the control arm) At 2nd interim analysis, the worst remaining experimental arm is dropped. More patients are randomized to the remaining arms (including the control arm) At final analysis, the last experimental arm is tested versus the control arm. A more commonly used approach is the p-value combination (Jaki 2013 & Bauer 1999). However, this approach is usually described for a single efficacy analysis. Extending the p-value combination approach based on the Group Sequential Design method and related alpha-spending functions may provide another good option to deal with treatment-selection during a Phase 2/3 study. Illustration of combination test: Final dataset is split into two independent subsets, using a population flag (patients being randomized before vs after dose-optimization) Primary analysis is performed on each subset separately (from which p-values are derived and ) Then the combined p-value can be derived as follows (Inverse normal test): , with and being the relative information provided by each subset. is finally compared to the -level to be spent on primary endpoint. This -level can be derived from a spending function (similarly to Group Sequential Design approach). The objective of the internship will be to: Drop-the-losers design, extend the methodology to binary and survival endpoints, extend the methodology to add futility stopping rules, P-value combination Extend the methodology to Group Sequential Design assess final estimator’s bias (a well-known issue for most of adaptive designs), compare the design’s characteristics with other approaches (adaptive randomization, parallel studies using GSD) through a simulation study. Wason J, Stallard N, Bowden J, Jennison C. A multi-stage drop-the-losers design for multi-arm clinical trials. Statistical Methods in Medical Research. 2017;26(1):508-524. doi:10.1177/0962280214550759 Jaki, T. and Magirr, D. (2013), Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all promising treatments. Statist. Med., 32: 1150-1163. https://doi.org/10.1002/sim.5669 Bauer, P. and Kieser, M. (1999), Combining different phases in the development of medical treatments within a single trial. Statist. Med., 18: 1833-1848. https://doi.org/10.1002/(SICI)1097-0258(19990730)18:14<1833::AID-SIM221>3.0.CO;2-3 Key Responsibilities The Student Intern will have to deliver an internship report, in alignment with requirements from his/her university. For BMS internal use, the student will develop a PowerPoint presentation of his/her work and findings, and a possible guidance document. Internship outcomes may lead to publications and further research. Qualifications & Experience Internship is 4-6 months, as part of the university. Offered to Master or PhD student in Statistics (Applied Mathematics/Statistics/Biostatistics). Required skills: Adaptive designs Multiple testing procedures Optional: Survival analysis Bayesian statistics (used for adaptive randomization) Good working knowledge of using R/SAS or other mathematical software and MS Office products (Word, Excel, PowerPoint). Ability to work both collaboratively as part of a team and independently. Good verbal communication skills, ability to write clearly and effectively, in English language. Strong interpersonal skills with professional attitude. Owning, or able to obtain, permit to work in Switzerland. Must not be employed at the time the internship starts. If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career. Uniquely Interesting Work, Life-changing Careers With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in an inclusive culture, promoting diversity in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues. On-site Protocol Physical presence at the BMS worksite or physical presence in the field is a necessary job function of this role, which the Company deems critical to collaboration, innovation, productivity, employee well-being and engagement, and it enhances the Company culture. BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to
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