Intern Biostatistics (m/f/x)

Passion for Innovation. Compassion for Patients.

With over 120 years of experience and more than 17,000 employees in over 20 countries, Daiichi Sankyo is dedicated to discovering, developing, and delivering new standards of care that enrich the quality of life around the world.

In Europe, we focus on two areas: The goal of our Specialty Business is to protect people from cardiovascular disease, the leading cause of death in Europe, and help patients who suffer from it to enjoy every precious moment of life. In Oncology, we strive to become a global pharma innovator with competitive advantage, creating novel therapies for people with cancer.

Our European headquarters are in Munich, Germany, and we have affiliates in 13 European countries and Canada.

For our headquarter in Munich we are seeking highly qualified candidates to fill the position

Intern Biostatistics (m/f/x)

The Position

Health Technology Assessment (HTA) agencies often require the extrapolation of overall survival (OS) beyond the follow-up period of clinical trials, introducing significant uncertainty, particularly when data maturity is limited. Extrapolations beyond the trial period are typically based on parametric models fitted to the observed trial data, the choice of which can significantly influence the projected long-term survival estimates. Thus, long-term survival is one of the main uncertainties in assessing the cost-effectiveness of oncology treatments.
Using external evidence to inform OS is one possible solution to enhance the accuracy and reduce the bias of long-term survival extrapolations. Sources of external evidence include other clinical trials in the same indication, real world data (RWD) from observational studies, registry databases, or expert elicitations. External evidence is increasingly used in HTA appraisals of oncology drugs.
Several methods for integrating external evidence have been proposed, including piecewise methods, where external evidence affects only estimates beyond a specific time point; Bayesian approaches, which incorporate external data as prior information or utilize Bayesian hierarchical models, including the method shown in McCarthy et al. (2024); and population-matching methods, such as propensity scores. However, there remains a lack of clarity regarding which methods are most appropriate and how they compare. The National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) Technical Support Document (TSD) 21, briefly discussed the use of external information in this context but did not provide guidance on which methods are preferred.
The aim of this internship is to explore and evaluate the different approaches to incorporate external data to enhance OS extrapolations in the context of HTA. A literature review should be performed to identify the possible analysis strategies (both in scientific literature and in HTA appraisals) with their advantages and limitations. The different statistical methods will be compared using clinical trial data and external data.
The duration of the internship is 6 months and can start now or according to agreement.

References

Rutherford MJ, Lambert PC, Sweeting MJ, Pennington B, Crowther MJ, Abrams KR, Latimer NR. NICE DSU Technical Support Document 21: Flexible Methods for Survival Analysis. London: National Institute for Health and Care Excellence (NICE); 2020 Jan.
Bullement A, Stevenson MD, Baio G, Shields GE, Latimer NR. A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment. Med Decis Making. 2023 Jul;43(5):610-620. doi: 10.1177/0272989X231168618. Epub 2023 Apr 26. PMID: 37125724; PMCID: PMC10336710.
McCarthy G, Young K, Madin-Warburton M, Mantaian T, Brook E, Metcalfe K, Mikelson J, Xu R, Seyla-Hammer C, Aguiar-Ibáñez R, Amonkar M. Cost-effectiveness of pembrolizumab for previously treated MSI-H/dMMR solid tumours in the UK. J Med Econ. 2024 Jan-Dec;27(1):279-291. doi: 10.1080/13696998.2024.2311507. Epub 2024 Feb 19. PMID: 38293714.

Personal skills and professional experience:

• Bachelor’s degree in Statistics, Mathematics, or similar
• Good programming skills in R and/or SAS
• Good knowledge of survival analysis, knowledge of Bayesian statistics is a plus
• Excellent oral and written communication skills
• Good command of English

Why work with us?

Working at Daiichi Sankyo is more than just a job – it is your chance to make a difference and change patients’ lives for the better. We can only achieve this ambitious goal together. That is why we foster a culture of mutual respect and continuous learning, with a strong commitment to inclusion and diversity. Here, you will have the opportunity to grow, think boldly, and contribute your ideas. If you have a proactive mindset and passion for addressing the needs of patients, we eagerly await your application.

For more information: www.daiichi-sankyo.eu