Biostatistician : Analysis of Recurrent Events in Chronic Diseases (internship)

Quinten est une société d’experts de l’Intelligence Artificielle au service de l’efficacité des métiers de l’entreprise. Elle bâtit depuis plus de 14 ans des solutions sur mesure d’aide à la décision tirant parti du plein potentiel des données avec un objectif clair et des bénéfices systématiques pour ses clients.

Quinten se différencie de ses concurrents directs, non seulement par sa longue expérience multisectorielle, en particulier en santé et en Banque/Assurance et ses choix technologiques résolument tournés vers l’augmentation de l’humain, mais aussi par sa capacité à concevoir, développer et industrialiser de véritables solutions d’aide à la décision, dont certaines sont utilisées par des milliers d’utilisateurs

Grâce à une capitalisation interne constante et une R&D à la pointe du marché, Quinten trouve les solutions qui font entrer la donnée dans l’amélioration de la performance qu’attendent ses clients. Enfin c’est une société partenaire stratégique de la transformation numérique de plusieurs entreprises de premier plan.

Context 

In chronic diseases, such as multiple sclerosis, chronic heart failure, asthma, etc., time-to-first event methods are generally clinically less meaningful as clinical events occurring after the first event are neglected [1].

Recurrent event methods have therefore been proposed to better capture patients’ disease burden and to benefit from a gain in information, as compared to conventional time-to-first methods. Those methods are being classified as either conditional, e.g., Andersen-Gill method, or marginal, e.g. Wei-Lin-Weissfeld method [2-4]. In such chronic conditions, the rate of terminal events, such as death, is generally very low, e.g., in multiple sclerosis. However, this rate is non-negligible in specific settings, such as chronic heart failure, hence ignoring them would lead to biased results [5-6].

The purpose of this internship will therefore be to review existing methods in the analysis of recurrent events without and with the presence of terminal events. Then, compare them appropriately in a simulation study under various specific scenarios reflecting real disease conditions and potentially apply them to an existing RWD, to be able to provide associated interpretations and make internal recommendations regarding all considered approaches. 

Objectives 

The master’s student is expected to: 

1. Conduct an up-to-date methodological literature review to identify weaknesses and strengths of recurrent event methods; 

2. Upon literature review conclusions, identify potential gaps/scenarios and if appropriate run a simulation study to evaluate strengths and weaknesses of comparable recurrent event methods according to specific disease conditions (at least in multiple sclerosis and if possible, in chronic heart failure); 

3. Apply identified methods of interest to RWD; 

4. Write a manuscript with the aim to be submitted in an international peer-reviewed journal. 

References 

1. Amorim LDAF, Cai J. Modelling recurrent events: a tutorial for analysis in epidemiology. Int J Epidemiol. 2015;44(1):324-333. doi:10.1093/ije/dyu222 

2. Andersen PK, Gill RD. Cox’s regression model for counting processes: A large sample study. Ann Stat. 1982;10(4):1100-1120. doi:10.1214/aos/1176345976 

3. Wei LJ, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J Am Stat Assoc. 1989;84(408):1065. doi:10.2307/2290084 

4. Būhler A. Comparison of time-to-first-event and recurrent event methods in multiple sclerosis trials. arXiv [statAP]. Published online 2021. http://arxiv.org/abs/2111.01937 

5. European Medicines Agency. Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses, https://www.ema.europa.eu/en/documents/other/qualification-opinion-clinically-interpretable-treatment-effect-measures-based-recurrent-event_en.pdf 

6. Ozga AK, Kieser M, Rauch G. A systematic comparison of recurrent event models for application to composite endpoints. BMC Med Res Methodol. 2018;18(1):2. doi:10.1186/s12874-017-0462-x

• Strong knowledge of biostatistics (Master program, engineering school in statistics or equivalent) 

• Excellent working knowledge in R statistical programming, Python is a plus 

• Interest in medical research 

• Fluency in written and oral scientific English

1. One meeting with HR

2. Technical use case

3. Meeting with Quinten Healthcare Manager