Postdoctoral Fellow, Epidemiology & Oncology RWE
Postdoctoral Fellow, Epidemiology & Oncology RWE
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Job Description
Therapeutic Area Description
The appropriate use of RealWorld Data (RWD) across the full cycle of drug development is essential in the pharmaceutical industry. Pharma R&D is increasingly using RWD in the premarket setting to inform clinical development programs and support regulatory submissions. At AstraZeneca (AZ) the clinical data sciences and realworld evidence (RWE) team within the broader AZ Oncology Data Science organization ensures all Phase 2/3 clinical trials from investment decisions through regulatory filing and commercialization are informed with robust Real World Evidence (RWE). While our daytoday work generates RWE for internal decisionmaking on clinical trial study design and interpretation (e.g. control arm selection expected control arm performance study population definitions trial interim analyses) our work is increasingly expected to impact regulatory submissions providing supportive and eventually pivotal evidence. Collected from various sources such as electronic health records and medical claims for nonresearch purposes RWD has limitations versus RCTs. As such we strive to use thorough study design and leverage the best methods to control numerous sources of bias that arise with nonrandomized study designs. Furthermore in oncology drug development databases of NGStested patients rich in molecular data coupled with clinical data have huge potential but require additional development and application of novel methods to understand their relevance to AZ trials.
This postdoc position has a duration of up to three years.
Role Summary
Do you have expertise in and a passion for the development and application of advanced epidemiology methods to ensure rigorous insights for patients Would you like to apply your expertise to impact early and late oncology clinical development in an organization that follows the science and turns ideas into lifechanging medicines
Purpose of this Position
To realize AstraZeneca oncologys longterm strategic goals to leverage Real World Data to accelerate drug development we seek a postdoctoral researcher to develop adapt and rigorously evaluate epidemiological methods for bias correction to ensure our study findings are relevant and comparable to clinical trials and ultimately fit to support FDA submissions.
Whats Involved in the Role
The postdoc will design and implement several demonstration projects testing approaches to correct or minimize the impact of various biases we encounter in our daytoday use of claims electronic health records and multimodal data. Results from these methods will be compared to AZs statusquo analytic approaches and validated against historical clinical trials SoC arms!
Why Should You Apply
You will have the opportunity to formulate and address key scientific questions motivated by AstraZenecas oncology portfolio strategy. These questions will drive innovative research of broad interest to the pharmacoepidemiology and clinicalgenomics scientific communities. You will produce highimpact publications on innovative research questions in the fields of pharmacoepidemiology and causal inference and simultaneously advance AstraZenecas use of RWE to accelerate and streamline drug development. Furthermore you will have access to extensive sources of RWD and clinical trial data and the support of teams with indepth knowledge of data sources clinical expertise and mentorship from internal and external methods experts.
Collaborations
Youll join a highly motivated fastpaced team of epidemiologists and data scientists committed to rigorous research bringing novel RWD insights to AstraZenecas Oncology pipeline. Your experience will be enriched through collaboration with a diverse internal community including statistical innovation applied data science artificial intelligence clinical teams and the broader community of AstraZeneca postdocs. In addition to the team at AstraZeneca you will have the opportunity to be mentored by two academic advisors Dr. Issa Dahabreh from the T.H. Chan Harvard School of Public Health and Dr. Camille Maringe at the London School of Hygiene and Tropical Medicine.
Essential Skills Experience & Behaviors
Solid understanding of epidemiological study design. Experience or knowledge of working with Real World Data such as claims electronic health records or experience with analyzing clinical trial data. Significant experience with bias and methods to correct bias such as propensity score weighting/matching instrumental variable analysis matching adjusted indirect comparisons etc. Familiarity with approaches for generalizability and transportability. Experience coding in R (or similar).
Essential Qualifications / Education
PhD in fields such as Epidemiology Pharmacoepidemiology Biostatistics Statistical Genetics Clinical Informatics Computer Science Health Informatics.
Desirable Skills Experience & Behaviors
- Oncology
- Understanding of drug development
- Understanding of causal inference Bayesian methods and/or quantitative bias analysis
- Prior experience in pharma clinical trials or engagement with regulators
Personal Characteristics & Behaviors
- Collaborative innovative curious goes above and beyond for patients.
We anticipate a high volume of applicants so do not delay apply today!
Compensation for this role is based on years of experience after obtaining PhD is a minimum of $107500 up to a maximum of $120000 shortterm incentive. Some relocation supplements depend on distance and location at the employers discretion.
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Date Posted
13May2025Closing Date
Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion starting with our recruitment process. We welcome and consider applications from all qualified candidates regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations please complete the section in the application form.Employment Type
Full-Time
