Lorenzo Trippa

Assistant Professor of Biostatistics

Department of Biostatistics

Research

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

 

Publications 2014

L. Trippa, G. Parmigiani, P. Wen, D. Berry, and B. Alexander.Combining PFS and OS as a novel composite endpoint for neuro-oncology trials.
Neuro-oncology, 2014

L. Trippa, G. Parmigiani, C. Huttenhower, and L. Waldron.Cross-study validation of prediction methods.
Annals of Applied Statistics (Accepted), 2014

E. Marco, R. Karp, G. Guo, P. Robson, A. Hart, L. Trippa, and Y. Guo-Cheng.Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.
Proceedings of the National Academy of Sciences – Accepted -, 2014

Y. Xu, L. Trippa, P. Mueller, and Y. Ji. Subgroup-based adaptive (suba) designs for multi-arm biomarker trials.
Statistics in Biosciences, 2014

S. Ventz and L. Trippa. Bayesian designs and the control of frequentist characteristics: A practical solution.
Biometrics, 2014

B. M. Alexander and L. Trippa. Progression-free survival: too much risk, not enough reward?
Neuro-oncology, 2014

E. B. Bourgeois, B. N. Johnson, A. J. McCoy, L. Trippa, A. S. Cohen, and E. D. Marsh. A toolbox for spatiotemporal analysis of voltage-sensitive dye imaging data in brain slices.
PloS one, 2014

B. M. Alexander, E. Galanis, W. A. Yung, K. V. Ballman, J. M. Boyett, T. F.Cloughesy, J. F. Degroot, W. Mason, et al.Brain malignancy steering committee clinical trials planning workshop: Report from the targeted therapies working group.
Neuro-oncology, 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, Accepted 2014

M. Riester, W. Wei, L. Waldron, A. Culhane, L. Trippa, 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, 2014

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, 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