Lorenzo Trippa

Assistant Professor of Biostatistics

Department of Biostatistics


I am particularly interested in the development of innovative adaptive clinical trial designs. My research includes the study of methodologies for the analysis of data generated by adaptive trials. I am also particularly interested in Bayesian nonparametric, a great source of modeling opportunity in biomedical applications as well as, both computational and theoretical problems.


Publications 2012-2014

S.Bacallado, S.Favaro, L.Trippa, “Bayesian nonparametric inference for shared species richness in multiple populations”,  Journal of Statistical Planning and Inference, 2014  accepted
C. Bernau, M. Riester, A. Boulestei, G. Parmigiani, C. Huttenhower, L. Waldron,and L. Trippa. Cross-study validation for assessment of prediction models and algorithms. Bioinformatics, Conditionally accepted 2014
M. Riester, W. Wei, L. Waldron, A. Culhane, L. Trippa, E. Oliva, S. Kim, F. Michor, C. Huttenhower, G. Parmigiani, and M. Birrer. Risk prediction for late-stage ovarian cancer by meta-analysis of 1,525 patient samples. Journal of the National Cancer Institute, In Press 2014
S. Wade, D. Dunson, S. Petrone, and L. Trippa. Improving prediction from
dirichlet process mixtures. Journal of Machine Learning Research (acepted),
S. Favaro, S. Bacallado, and L. Trippa. Looking-backward probabilities for
gibbs-type exchangeable random partitions. Bernoulli, 2014
G. Parmigiani, S. Boca, J. Ding, and L. Trippa. Statistical tools and r software
for cancer driver probabilities. In Gene Function Analysis, pages 113{134.
Springer, 2014
J. Wason and L. Trippa. A comparison of bayesian adaptive randomization and
multi-stage designs for multi-arm clinical trials. Statistics in medicine, 2014
J. Lee, F. A. Quintana, P. Muller, and L. Trippa. De ning predictive probability
functions for species sampling models. Statistical science: a review journal of
the Institute of Mathematical Statistics, 28(2):209, 2013
S. Bacallado, S. Favaro, L. Trippa, et al. Bayesian nonparametric analysis of
reversible markov chains. The Annals of Statistics, 41(2):870{896, 2013
L. Trippa, E. Q. Lee, P. Y. Wen, T. T. Batchelor, T. Cloughesy, G. Parmigiani,
and B. M. Alexander. Reply to b. freidlin et al. Journal of Clinical Oncology,
31(7):970{971, 2013
B. M. Alexander, P. Y. Wen, L. Trippa, D. A. Reardon, W.-K. A. Yung,
G. Parmigiani, and D. A. Berry. Biomarker-based adaptive trials for patients
with glioblastoma lessons from i-spy 2. Neuro-oncology, 15(8):972{978, 2013
J. Ding, L. Trippa, X. Zhong, G. Parmigiani, et al. Hierarchical bayesian analysis
of somatic mutation data in cancer. The Annals of Applied Statistics, 7(2):
2883{903, 2013
G. Parmigiani and L. Trippa. Comment on Article by Muller and Mitra.
Bayesian Analysis, 8(2):346{347, 2013
L. Trippa, E. Q. Lee, P. Y. Wen, T. T. Batchelor, T. Cloughesy, G. Parmigiani,
and B. M. Alexander. Bayesian adaptive randomized trial design for patients
with recurrent glioblastoma. Journal of Clinical Oncology, 30(26):3258{3263,
L. Trippa, G. L. Rosner, and P. Muller. Bayesian enrichment strategies for randomized
discontinuation trials. Biometrics, 68(1):203{211, 2012c
L. Trippa, G. L. Rosner, and P. Muller. Rejoinder. Biometrics, 68(1):224{225,
L. Trippa and S. Favaro. A class of normalized random measures with an exact
predictive sampling scheme. Scandinavian Journal of Statistics, 39(3):444{460,