Publications

Journal articles in preparation (pre-print)

  1. Caldera, L., Cappozzo, A., Masci, C., , Forlani, M., Antonelli, B., Leoni, O., Paganoni, A. M., Ieva, F. (2025+). ‘Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients’. Submitted, arXiv

  2. Nicolussi, F.; Masci, C. (2025+). ‘Stratified Multilevel Graphical Models: Examining Gender Dynamics in Education’. Submitted, MOX-Report preprint

  3. Ragni, A.; Romani, G., Masci, C. (2025+). ‘TimeDepFrail: Time-Dependent Shared Frailty Cox Models in R’. Submitted, arXiv

  4. Bergonzoli, G.; Rossi, L.; Masci, C. (2025+). ‘Ordinal Mixed-Effects Random Forest’. Submitted, arXiv

  5. Masci, C., Spreafico, M., Ieva, F. (2025+). ‘Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients’. Submitted, arXiv

Articles in Peer-Reviewed Journals (Statistics)

  1. Ragni, A.; Masci, C.; Paganoni, A.M. (2026). ‘Analysis of Higher Education Dropouts Dynamics through Multilevel Functional Decomposition of Recurrent Events in Counting Processes’. Journal of the Royal Statistical Society: Series C, in press, paper

  2. Caldera, L.; Masci, C.; Cappozzo, A.; Forlani, M.; Antonelli, B.; Leoni, O. and Ieva, F. (2025). ‘Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel Multilevel logistic cluster-weighted modeling approach’. Biometrics, 81(2), paper

  3. Lema, M. L. D.; Masci, C., Soncin, M.; Agasisti, T. (2025). ‘Risky decline? Exploring the determinants of pupil’s proficiency development over time.’ Socio- Economic Planning Sciences, 99, 102207. paper

  4. Ragni, A.; Masci, C., Ieva, F.; Paganoni, A.M. (2025). ‘A Statistical Significance-Based Approach for Clustering Grouped Data via Generalized Linear Model with Discrete Random Effects’. Journal of the Royal Statistical Society: Series A, paper

  5. Masci, C.; Ieva, F.; Paganoni, A.M. (2024). ‘Inferential Tools for Assessing Dependence Across Response Categories in Multinomial Models with Discrete Random Effects’. Journal of Classification, 1-29. paper

  6. Ragni, A., Ippolito, D., Masci, C. (2024). ‘Assessing the Impact of Hybrid Teaching on Students’ Academic Performance via Multilevel Propensity Score-based techniques’. Socio-Economic Planning Sciences, paper

  7. Masci, C., Cannistrà, M., & Mussida, P. (2023). ‘Modelling time-to-dropout via shared frailty Cox models. A trade-off between accurate and early predictions’. Studies in Higher Education, 1-19.paper

  8. Masci, C., Ieva, F. and Paganoni, A.M. (2022). ‘Semiparametric multinomial mixed-effects models: a university students profiling tool.’ The Annals of Applied Statistics, 16 (3): 1608 - 1632.paper

  9. Cannistrà M., Masci, C., Ieva, F., Agasisti T. and Paganoni, A.M. (2021). ‘Early-predicting dropout of university students: an application of innovative ma- chine learning and multilevel statistical techniques’. Studies in Higher Education, 1-22. paper

  10. Fontana, L., Masci, C., Ieva, F. and Paganoni, A.M. (2021). ‘Performing learning analytics via generalized mixed-effects trees’. Data, 6, 74. paper

  11. Masci, C., Ieva, F., Agasisti, T. and Paganoni, A.M. (2021). ‘Evaluating class and school effects on the joint achievements in different subjects: a bivariate semiparametric mixed-effects model’. Computational Statistics, 36(4), 2337-2377.paper

  12. Pellagatti M., Masci, C., Ieva, F. and Paganoni, A.M. (2021). ‘Generalized mixed-effects random forest: A flexible approach to predict university student dropout’. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(3), 241-257. paper

  13. Masci, C., Ieva, F. and Paganoni, A.M. (2018). ‘Semi-parametric mixed-effects models for unsupervised classification of Italian schools’. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182.4, pp. 1313-1342.paper

  14. Masci, C., Agasisti, T. and Johnes, G. (2018). ‘Student and school performance across countries: A machine learning approach’. European Journal of Operational Research, 269(3), pp. 1072-1085. paper

  15. Masci, C., Ieva, F., Agasisti, T. and Paganoni, A.M. (2017). ‘Bivariate multi- level models for the analysis of mathematics and reading pupils’ achievements’. Journal of Applied Statistics, 44.7, pp. 1296–1317. paper

  16. Masci, C., De Witte, K. and Agasisti, T. (2016). ‘The influence of school size, principal characteristics and school management practices on educational performance: An efficiency analysis of Italian students attending middle schools’. Socio-Economic Planning Sciences (61), pp. 52-69.paper

  17. Masci, C., Ieva, F., Agasisti, T. and Paganoni, A. M. (2016). ‘Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students’. Socio-Economic Planning Sciences (54), pp. 47-57.paper

Articles in Peer-Reviewed Journals (Cross-disciplinary)

  1. Asadi, F, Homayounfar, R., Mehrali, Y., Masci, C., Zayeri, F. (2025). ‘Innovative Statistical method for longitudinal and hierarchical data modeling: The GMEXGBoost Method’. BMC Medical Research Methodology, in press, paper.

  2. Lurani Cernuschi, A.; Masci, C.; Corso, F.; Muccini, C.; Ceccarelli, D.; Galli, L.; Ieva, F.; Paganoni, A.M.; Castagna, A. (2025). ‘A neural-network approach for predicting time to cardiovascular diseases in HIV patients based on real-world data’. Operational research, paper

  3. Karimi Ghahfarokhi, M., Zayeri, F., Khalili, D., Masci, C., Sheidaei, A., Mehrali, Y., Masaebi, F., Mehrabi, Y. (2025). ‘Application of generalized linear mixed effects random forest for identifying risk factors of prediabetes in Tehran Lipid and Glucose Study’. Scientific Reports, 15(1), 41361, paper

  4. Asadi, F.; Homayounfar, R.; Mehrali, Y.; Masci, C.; Yaseri, M.; Talebi, S.; Zayeri, F. (2024). ‘Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms’. Scientific Reports, 14, 22230 (2024), paper

  5. Bertoletti A., Cannistrà M., Diaz Lema M., Masci, C., Mergoni A., Rossi L., Soncin M. (2023). ‘The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest’. Economies 11:32, paper

  6. Fiz, F., Masci, C., Costa, G., Sollini, M., Chiti, A., Ieva, F., Torzilli, G. & Viganò, L. (2022) ‘LPET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival’, European Journal of Nuclear Medicine and Molecular Imaging, pp 1-14.paper

  7. Costa, G., Cavinato, L., Masci, C. et al. (2021). ‘Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases’. Cancers. paper

  8. Schiltz, F., Masci, C., Agasisti, T. and Horn, D. (2018). ‘Using regression tree ensembles to model interaction effects: a graphical approach’. Applied Economics, 50(58), pp. 6341-6354.paper

Books and Book Chapters

  1. Masci, C. (2019). Semi-parametric mixed-effects models for assessing public education systems. Doctoral dissertation, Italy.
  2. Agasisti, T., Ieva, F., Masci, C., Paganoni, A.M., Soncin, M. (2017). Using Statistical Analytics to Study School Performance through Administrative Datasets. In Data Analytics Applications in Education, Auerbach Publications, 183-209.
  3. Ieva, F., Masci, C., Paganoni, A.M. (2016). Laboratorio di statistica con R (2/Ed. con MyLab e eText). Pearson Education.

Conference Proceedings

(Full list from 2016 – 2025)

  1. Masci, C. and Cappozzo, A. (2025). A novel cluster-weighted multilevel model for two-levels clustering . CFE-CM Statistics 2025. ECOSTA Econometrics and Statistics, ISBN 978-9925-7812-9-4.

  2. Nicolussi, F., Masci, C., Bertarelli, G. and Mecatti, F. (2025). Learning Procedure for Partially Observed Variables . Scientific Meeting of the Italian Statistical Society. Springer Nature Switzerland.

  3. Nicolussi, F., Masci, C. (2024). Stratified Multilevel Graphical Models: a gender perspective in Education. Methodological and Applied Statistics and Demography II, SIS 2024, Pearson, 657-662.

  4. Masci, C., Cappozzo, A., Ieva, F., Leoni, O., Forlani, M., Antonelli, B., Paganoni, A.M. (2024). Model-Based Clustering of Nested Lifetime Data: Profiling COVID-19 Heart Failure Patients. SIS 2024 Short Papers, Pearson, 240-245.

  5. Ragni, A., Masci, C., Paganoni, A.M. (2024). Analysis of Higher Education Dropout Dynamics Through Functional Decomposition of Recurrent Events on Time-to-Event Processes. SIS 2024 Short Papers, Pearson, 461-466.

  6. Masci, C., Spreafico, M., Ieva, F. (2023). Joint modelling of hospitalizations and survival in Heart Failure patients: a discrete non parametric frailty approach. SIS 2023 Short Papers, Pearson, 375-380.

  7. Ragni, A., Masci, C., Ieva, F., Paganoni, A.M. (2023). A novel statistical-significance based semi-parametric GLMM for clustering countries standing on their innumeracy levels. SIS 2023 Short Papers, Pearson, 939-944.

  8. Masci, C., Ieva, F., Paganoni, A.M. (2022). Multinomial Multilevel Models with Discrete Random Effects: a Multivariate Clustering Tool. IFCS 2022 Book of Abstracts, Oporto.

  9. Masci, C., Giovio, M., Mussida, P. (2022). Survival models for predicting student dropout at university across time. END 2022 Short Papers Vol. I, inScience Press, 203-207.

  10. Masci, C., Giovio, M., Mussida, P. (2022). Modelling time to university dropout by means of time-dependent frailty Cox PH models. SIS 2022 Short Papers, Pearson, 1771-1776.

  11. Masci, C., Ieva, F., Paganoni, A.M. (2021). Multinomial semiparametric mixed-effects model for profiling engineering university students. SIS 2021 Short Papers, Pearson, 1481-1486.

  12. Ieva, F., Baroni, G., Cavinato, L., Masci, C., Costa, G., Fiz, F., Chiti, A., Viganò, L. (2021). Virtual biopsy in action: a radiomic-based model for CALI prediction. Book of Short Papers SIS 2021, Pearson, 318-323.

  13. Cannistrà, M., Agasisti, T., Paganoni, A.M., Masci, C. (2021). How Much Tutoring Activities May Improve Academic Careers of At-Risk Students? An Evaluation Study. SIS 2021 Short Papers, Pearson, 318-323.

  14. Masci, C., Ieva, F., Agasisti, T., Paganoni, A.M. (2019). Bivariate semiparametric mixed-effects models for classifying the effects of Italian classes on multiple student achievements. CLADAG 2019 Short Papers, 971-976.

  15. Masci, C., Ieva, F., Agasisti, T., Paganoni, A.M. (2019). Classification of Italian classes via bivariate semiparametric multilevel models. SIS 2019 Short Papers, Pearson, 971-976.

  16. Masci, C., Agasisti, T., Ieva, F., Paganoni, A.M. (2018). Unsupervised clustering of Italian schools via non-parametric multilevel models. SIS 2018 Short Papers, Pearson, 1-6.

  17. Masci, C., Agasisti, T., Ieva, F., Paganoni, A.M. (2017). Nonparametric mixed-effects model for unsupervised classification in the Italian education system. CLADAG 2017 Short Papers, Universitas Studiorum.

  18. Masci, C., Ieva, F., Agasisti, T., Paganoni, A.M. (2016). Analysis of pupils’ INVALSI achievements by means of bivariate multilevel models. SIS 2016 Short Papers, Pearson, 1-6.

Open-Source Software

  1. Ragni, A., Romani, G., Masci, C. (2024). TimeDepFrail: Time-Dependent Shared Frailty Cox Models in R. R package
  2. Masci, C., Ragni, A., Ieva, F., Paganoni, A.M. (2024). SpMEMs: an R package for semi-parametric mixed-effects models. Work in progress.

Book Editing

  1. De Battisti, F., Leorato, S., Masci, C., Nicolussi, F. (2025). Statistical Methods for Data Analysis and Decision Sciences (SDS 2025). Springer, Switzerland. (https://link.springer.com/book/9783032189875, print ISBN: 978-3-032-18987-5, eBook ISBN: 978-3-032-18988-2)