Insegnamenti a.a. 2021/2022
20486 FONDAMENTI DI BUSINESS ANALYTICS / PRINCIPLES OF BUSINESS ANALYTICS
40954 TOPICS IN EXPERIMENTAL RESEARCH
41006 APPLIED SURVIVAL DATA ANALYSIS
*** DOWNLOADABLE CV: sites.google.com/site/marcobonettipubs/cv-mb ***
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Laureato in Scienze statistiche ed economiche presso l'Università La Sapienza di Roma (1991).
MS. in Statistica (1994), University of Connecticut, Storrs, CT.
Ph.D. in Statistica (1996), University of Connecticut, Storrs, CT. Ph.D.
Postdoctoral fellow in Biostatistica, Harvard University e Dana-Farber Cancer Institute, Boston, U.S.A. (1996-1998).
At Bocconi University:
- (Bocconi) Full Professor of Statistics (2012-)
- Professore Associato di Statistica (2010-)
- Bocconi Associate Professor of Statistics (2008-2010)
- Assistant Professor of Statistics (2004-2008)
- Director, MS program in Economic and Social Sciences (2008-)
- Fellow of the research centers CERGAS and Dondena (2008-)
Adjunct Assistant Professor of Biostatistics (2004-2005) - Department of Biostatistics, Harvard University (Boston, U.S.A.).
Assistant Professor of Biostatistics (2000-2004) - Department of Biostatistics, Harvard University and Department of Biostatistical Science, Dana-Farber Cancer Institute, (Boston, U.S.A.). Secondary appointment as Instructor within the Harvard Medical School and The Children's Hospital (Boston, U.S.A.). Biostatistician for the International Breast Cancer Study Group (1996-2004) and for the Eastern Cooperative Oncology Group (2000-2002).
Research Associate (1999-2000), Postdoctoral Fellow (1996-98) - Department of Biostatistics, Harvard University and Department of Biostatistical Science, Dana-Farber Cancer Institute, Boston, U.S.A.
Instructor (Summers 1994-96), Research Assistant and Unix System Administrator (1992-96) - Department of Statistics, University of Connecticut, U.S.A.
Statistical Consultant (Summer 1993) - University of Connecticut Health Center.
Aree di interesse scientifico
Analysis of experimental data: Survival analysis; Treatment effect heterogeneity; Clinical trials in oncology; Methods for missing data problems.
Biosurveillance: Interpoint-distance based methods; Risk in health.
Ph.D. Dissertation Geometric methods in data analysis. Advisor: Professor Richard Vitale.
Scopus h-index (February 17th, 2013): 14
(per la lista completa e per scaricarle si segua il link "PERSONAL PAGE")
Bonetti, M., Piccarreta, R., and Salford, G. (2013). Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns. In press, Demography.
Gigliarano, C. and Bonetti, M. (2013). The Gini Test for Survival Data in Presence of Small and Unbalanced Groups. In press, Epidemiology, Biostatistics and Public Health. (DOI: 10.2427/8762).
Baker, S.G., Kramer, B.S., Sargent, D.J., and Bonetti, M. (2012). Biomarkers, subgroup evaluation, and clinical trial design. Discovery Medicine 13(70): pp. 187-92.
Tebaldi, P., Bonetti, M., and Pagano, M. (2011). M statistic commands: interpoint distance distribution analysis. In Press, The Stata Journal. [Code available at econpapers.repec.org/software/bocbocode/S457257.htm].
Lazar, A.A., Cole, B.F., Bonetti, M. Gelber, R.D. (2010). Evaluation of Treatment–Effect Heterogeneity Using Biomarkers Measured on a Continuous Scale: Subpopulation Treatment-Effect Pattern Plot (STEPP). Journal of Clinical Oncology (Statistics in Oncology series), 28(29); 4539–4544.
Gunnarsdo ttira,K.A.,Jensena,M.-B.,Zahrieh,D.,Gelber,R.D.,Knoopa,A.,Bonetti,M.,Mourid- sena, H. and Ejlertsena, B. (2010). CEF is superior to CMF for tumours with TOP2A aberrations: A Subpopulation Treatment Effect Pattern Plot (STEPP) Analysis on Danish Breast Cancer Cooperative Group Study 89D. Breast Cancer Research and Treatment, 123(1), pp. 163–169.
Bonetti, M., Gigliarano, C. and Muliere, P. (2009). The Gini Concentration Test for Survival Data. Lifetime Data Analysis, doi: 10.1007/s10985-009-9125-5.
Dallolio, L., Bellocco, R., Richiardi, L., Fantini, M.P. and the Causal Inference in Epidemiology (ICE) SISMEC Working Group (2009). Using directed acyclic graphs to understand confounding in observational studies. Biomedical Statistics and Clinical epidemiology, 3(2); 89–96.
Forsberg White, L., Bonetti, M. and Pagano, M. (2009). The choice of the number of bins for the M statistic. Computational Statistics and Data Analysis, 53(10):3640–3649.
Park, P.J., Manjourides, J., Bonetti, M. and Pagano M. (2009). A permutation test for determining significance of clusters with applications to spatial and gene expression data, Computational Statistics and Data Analysis, 53(12):4290–4300.
Bonetti, M., Zahrieh, D., Cole, B.F. and Gelber, R.D. (2009). A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data. Statistics in Medicine, 28(8):1255–68.
Bonetti, M., Olson, K.L., Mandl, K.D. and Pagano, M. (2008). Parametric modelling of interpoint distance distributions, with an application to a mixture model for biosurveillance data. Biomedical Statistics and Clinical Epidemiology, 2(3); 255–266.
Bonetti, M., Cole, B.F. and Gelber, R.D. (2008). Another STEPP in the right direction. Letter to the Editor, Journal of Clinical Oncology, 26(22):3813–3814.