Thesis Work, 30 Credits - Choosing follow-up duration and sample size in negative-binomial recurrent

the Opportunity: As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business. Thesis work description: In recurrent-event clinical trials, both the number of patients and the follow-up duration influence the reliability and efficiency of the study. Longer follow-up may provide additional information, but can also increase patient burden, study duration, and resource requirements. The aim of this thesis is to investigate how to optimize the duration and sample size of variable-duration clinical trial designs for recurrent events analyzed using negative-binomial models. You will evaluate how much additional value is gained by following patients for longer and identify when extended follow-up no longer provides meaningful benefits. Through statistical modelling and simulation, you will investigate different recruitment patterns and patient populations, and develop practical guidance for designing future recurrent-event clinical trials. The results will support the development of reliable and efficient clinical trial designs while minimizing patient burden and study resources. Placement: This is an on-site position at AstraZeneca Gothenburg. Please note, AstraZeneca does not support with accommodations for this role Structure: Duration: Spring 2027 Credits: 30 Essential Requirements: Enrolled in a Master’s programme in statistics, mathematics, or engineering with a mathematical or statistical component Experience with programming, preferably in R. Interest in statistical modelling, clinical trials, and biostatistics. Basic knowledge of generalized linear models (GLMs) and experience with statistical simulation are advantageous. Excellent English communication skills, including scientific writing and presentations. So, what’s next? Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfil your potential. We can´t wait to hear from you! We welcome your application as soon as possible, but ahead of the scheduled closing date October 29, 2026 . In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published.