Embedded Files
"Bayesian Methods for Clinical and Observational Studies" was the winning project of the University of Florence Young Independent Researcher call, funded by NextGenerationEU (BayesMeCOS Grant no. B008-P00634).
It all started in 2022 when Alejandra Avalos-Pacheco, Matteo Pedone, and I were at the DiSIA @Unifi. Despite being involved in different projects with different collaborators, we were all directly and indirectly facing the issue of how the traditional development of new drugs and biomarkers following the “one drug, one target disease” strategy is costly and inefficient. Hence, we thought it would have been helpful to research together to create Bayesian Methods for Clinical and Observational Studies that study heterogeneous populations, target several diseases, and test various treatments from the pre-clinical and clinical stages. We then proposed the project intending to help speed up the drug development process while increasing the power of results and biomarker discovery using adaptive randomization. Since then, our research group has grown, first welcoming Norma Alejandra Vergara Lope Gracia to the team and then Giulio Grossi.
The project developed in three directions:
Precision Medicine and adaptive designs: integrate data coming from non-concurrent trials; change the randomization ratios using covariate-dependent models;
External Data Integration: provide a way to analyze the study in the presence of intercurrent events, i.e., events that take place after the randomization and may bias the study results in a causal framework;
help evaluate the potential variations in global genomic architecture associated with bacterial genomic information.
research products
research products
Ballerini V., Liseo B. (2024) Inferring a population composition from survey data with nonignorable nonresponse: Borrowing information from external sources. Journal of Survey Statistics and Methodology. Accepted for publication [Read it here: arXiv:2210.08346v2]
Ballerini V., Grossi G. (2024) Synthetic control method for clinical trials and precision medicine. Book of Short Papers 2024, forthcoming
Ballerini V., Mattei A., Mealli F. (2024) Principal stratum strategy for safety evaluation under principal ignorability. Book of Short Papers 2024, forthcoming
Avalos-Pacheco A., Ballerini V., Pedone M., Müller P. (2023) Contributed discussion: "Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score (with Discussion)", Bayesian Analysis, 18(2), 680-682.
Ballerini V., Bornkamp B., Mattei A., Mealli F., Wang C., Zhang Y. (2023) Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation due to adverse events. arXiv:2310.06653v1
Braito L., Ballerini V., Bocci C., Rocco E. (2024) “Integrating traditional and innovative data sources”, report. Available here
Mattei A., Ding P., Ballerini V., Mealli F. (2024) Assessing causal effects in the presence of treatment switching through principal stratification. Bayesian Analysis, Advance Publication 1 - 28. https://doi.org/10.1214/24-BA1425
Niccolai E., Pedone M., Martinelli, I. . . ., Stingo F.C., Mandrioli J., and Amedei A. (2024). Amyotrophic lateral sclerosis stratification: Unveiling patterns with virome, inflammation, and metabolism molecules. Journal of Neurology. 1-16. 10.1007/s00415-024-12348-7
Pedone M., Argiento R., Stingo F. C. (2023) Personalized treatment selection model for survival outcomes. Book of Abstract and Short Papers, 14th Scientific Meeting of the Classification and Data Analysis Group. Salerno, September 11-13, 2023 edited by Carla Rampichini, Michele La Rocca, Pietro Coretto, Giuseppe Giordano, Maria Lucia Parrella. Available here
Page updated
Google Sites
Report abuse