Email Address: firstname.lastname@example.org
I am a Research Assistant at the MRC Biostatistics Unit, working with Sofia Villar on multi-armed bandits (MABs) in randomized experiments. More specifically, my focus is on the emergent area of mobile-Health (m-Health) and how to optimally design mobile apps for guiding users’ behavior and improving their health outcomes. The design of such applications involves both the design of the micro-randomized trial (MRT) and the design of the bandit algorithm to be used for optimizing the just-in-time adaptive interventions (JITAIs). As experiments with m-Health apps usually have a dual goal of improving outcomes for users enrolled into the experiment and learning about the effectiveness of interventions, estimation and inference in adaptively collected data also play a central role in my research arena.
Currently, I am also a PhD Candidate in Methodological Statistics at University of Rome La Sapienza (Italy). During my PhD, I joined as a Visiting Research Scholar Bibhas Chakraborty’s lab at the National University of Singapore (Singapore), and Joseph J. Williams’s Intelligent Adaptive Interventions Lab at the Computer Science Department of University of Toronto (Canada). My research topics during my PhD lied at the intersection between Bayesian methods, statistical reinforcement learning & multi-armed bandits, and modern real-world applications based on adaptive decision making. These include dynamic treatment regimes (DRTs), m-Health, and adaptive experimentations.
In a previous life, I worked as a biostatistician at the GIMEMA Foundation (Rome), and I have been actively involved in cancer and health-related research projects, included European Organisation for Research and Treatment of Cancer (EORTC) projects.
- Figueroa, C., Aguilera, A., Chakraborty, B., Modiri, A., Aggarwal, J., Deliu, N., Sarkar, U., Williams, J. J., Lyles, C., Design decisions when using reinforcement learning to optimize behavioral health interventions delivered via smartphones. Accepted submission for AMIA 2020 Virtual Annual Symposium
- Cottone, F., Deliu, N., Collins, G.S., Anota, A., Bonnetain, F., Van Steen, K., Cella, D. and Efficace, F., 2019. Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology. Quality of Life Research, 28(5), pp.1315-1325.
- Sparano, F., Aaronson, N.K., Cottone, F., Piciocchi, A., La Sala, E., Anota, A., Deliu, N., Kieffer, J.M. and Efficace, F., 2019. Clinician-reported symptomatic adverse events in cancer trials: are they concordant with patient-reported outcomes?. Journal of comparative effectiveness research, 8(5), pp.279-288.
- Cottone, F., Collins, G.S., Giesinger, J.M., Sommer, K., Deliu, N., Vignetti, M., Anota, A. and Efficace, F., 2019. The prognostic value of patient-reported outcomes (PROs) for survival outcomes in cancer patients: A systematic review. Journal of Clinical Oncology 2019 37:15_suppl, e18223-e18223
- Ousmen, A., Touraine, C., Deliu, N., Cottone, F., Bonnetain, F., Efficace, F., Brédart, A., Mollevi, C. and Anota, A., 2018. Distribution-and anchor-based methods to determine the minimally important difference on patient-reported outcome questionnaires in oncology: a structured review. Health and quality of life outcomes, 16(1), p.228.
- Deliu, N., Cottone, F., Collins, G.S., Anota, A. and Efficace, F., 2018. Evaluating methodological quality of Prognostic models Including Patient-reported HeAlth outcomes iN oncologY (EPIPHANY): a systematic review protocol. BMJ open, 8(10), p.e025054.