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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.
Title of Project: Speeding up the Clinical studies with Biomarker-based Randomization or Adaptive Bayesian Design with Biomarker Enrichment.
Identification of biomarker-based patient groups enables delivering the right medicine at a right moment. Multiple ongoing BMS (Bristol Myers Squibb) studies use biomarker-stratified groups in randomization (e.g., PD-L1 in CA209-648 and CA209-227) or target data collection for further biomarker development (e.g., CD8 in CA209-9N9). It usually takes several years of early development before a biomarker can be used as a criterion for group assignment in a clinical trial, which, for continuous biomarkers, requires learning the threshold value to define group(s). For example, PD-L1 is a well-established biomarker in lung cancer and breast cancer, both CA209-227 and CA209-648 studies use PD-L1 stratified design with threshold 1% vs. >1% to test, among others, the hypothesis about the investigational treatment performance in PD-L1 expressed and non-expressed subgroups.
It is challenging to statistically elaborate on the choice of the threshold value to the regulatory agencies. Indeed, with “naïve” approach and limited number of outputs we usually evaluate a sparse number of values, for example, 1% or 2% for CD8 in CA209-9N9, 1% and/or 50% for PD-L1 in CA209-227, this early dichotomization leads to ignoring actual distribution of the continuous values as well as the “grey zone” of values with potential supportive argument. Most of the time, the final threshold choice remains carried over to the next study with an asterisk “the proportion of subjects with a given biomarker expression will be monitored and may be re-assessed” that may become expensive for practical implementation.
In this work we propose to evaluate different scenarios of choosing biomarker thresholds with simulations inspired by existing studies. Using CA209-9N9, CA209-227 and CA209-648 as motivational examples, we would like to address the following design question
- For the given threshold values, considered sample sizes and similar biomarker distributions, what is more advantageous in terms of the number of recruited patients and constructing sound statistical argument: introduce a threshold for continuous biomarker early and use it for the randomization blocks or consider an adaptive (Bayesian) biomarker enrichment design?
The goal of the work would be a thorough description and analysis of the above-cited BMS use-cases and proposal of appropriate outputs for thresholds evaluation, depending on the qualifications of the intern, we would like to orient ourselves to an eventual extension of the statistical methodologies similarly but not limited to the following two articles.
Simon N, Simon R. Adaptive enrichment designs for clinical trials. Biostatistics. 2013 Sep;14(4):613-25. doi: 10.1093/biostatistics/kxt010. Epub 2013 Mar 21. PMID: 23525452; PMCID: PMC3769998.
Ruitao Lin, Peter F. Thall & Ying Yuan (2021) BAGS: A Bayesian Adaptive Group Sequential Trial Design with Subgroup-Specific Survival Comparisons, Journal of the American Statistical Association, 116:533, 322-334, DOI: 10.1080/01621459.2020.1837142.
Skills/Knowledge Required (technical and soft skills)
- Capacity to understand, reproduce and implement Bayesian statistical methodology in R or SAS,
- Capacity to conceive and implement simulations design in R or SAS.
Anticipated/expected outcome of the project (how would you define a successful internship?)
- A theoretical hint on how to speed up a clinical trial with an adaptive enrichment design
Benefits to the candidate
- Acquiring skills and references in a high applicability topic in biostatistics
- Networking and work experience
Benefits to BMS
- Evaluate statistical methodology possibility to speed up a clinical study where subgroup selection is biomarker based and threshold must be learned prior or in parallel with recruitment
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 adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.
BMS cares about your well-being and the well-being of our staff, customers, patients, and communities. As a result, the Company strongly recommends that all employees be fully vaccinated for Covid-19 and keep up to date with Covid-19 boosters.
BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.
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