[225] Samur AA, Mehmet Kemal Samur and SM, Magrangeas F, Fulciniti M, Szalat R, Richardson
PG, Anderson KC, Attal M, Moreau P, Parmigiani G, Avet-Loiseau H, Munshi NC. A detailed
alternate splicing landscape in multiple myeloma with significant potential biological and clinical
implications. Blood 128(22); 356 2016.
[226] Samur MK, Singh I, Shih-Han L, Sperling AS, Fulciniti M, Tai YT, Parmigiani G, Leslie CS, Mayr
C, Munshi NC. 3’ untranslated region (UTR) alterations are frequently targeted by MM-related
mirnas and affects the clinical outcome. Blood 128(22); 4447 2016.
[227] Gerke T, Tyekucheva S, Mucci L, Parmigiani G. Logistic push: a regression framework for partial
auc optimization. arXivorg 2016.
[228] Walker BA, Samur MK, Mavrommatis K, Ashby C, Wardell CP, Ortiz M, Towfic F, Stein CK,
Bauer MA, Amatangelo M, Parmigiani G, Yu Z, Trotter M, Avet-Loiseau H, Jackson GH, Anderson
KC, Thakurta A, Munshi NC, Morgan GJ. The multiple myeloma genome project: Development
of a molecular segmentation strategy for the clinical classification of multiple myeloma. Blood
128(22); 196 2016.
[229] Harrington D, Parmigiani G. Adaptive randomization of neratinib in early breast cancer. NEJM
375(16); 1593–4 2016.
[230] Ventz S, Barry WT, Parmigiani G, Trippa L. Bayesian response-adaptive designs for basket trials.
Biomet epub ahead of print 2017.
[231] Edefonti V, Parmigiani G. Combinatorial mixtures of multiparameter distributions: an application
to bivariate data. Int J 13(1) 2017.
[232] Tanguturi SK, Trippa L, Ramkissoon SH, Pelton K, Knoff D, Sandak D, Lindeman NI, Ligon AH,
Beroukhim R, Parmigiani G, Wen PY, Ligon KL, Alexander BM. Leveraging molecular datasets for
biomarker-based clinical trial design in glioblastoma. Neuro Oncol doi: 10.1093/neuonc/now312
2017.
[233] Ventz S, Parmigiani G, Trippa L. Combining bayesian experimental designs and frequentist data
analyses: motivations and examples. Applied Stochastic Models in Business and Industry doi:
10.1002/asmb.2249 2017.
[234] Braun D, Gorfine M, Parmigiani G, Arvold ND, Dominici F, Zigler C. Propensity scores with
misclassified treatment assignment: a likelihood-based adjustment. Biostat April 2017.
[235] Antonelli J, Parmigiani G, Dominici F. High dimensional confounding adjustment using continuous
spike and slab priors. arXivorg https://arxiv.org/pdf/1704.07532.pdf 25 Apr 2017.
[236] Braun D, Gorfine M, Katki HA, Ziogas A, Parmigiani G. Nonparametric adjustment for mea-
surement error in time to event data: Application to risk prediction models. JASA accepted
2017.
[237] Fulciniti M, Samur M, Samur A, Lin C, Parmigiani G, Anderson KC, Munshi N. Splicing fa-
tor SRSF1 is dsregulated in multiple myeloma with functional and clinical significance. Clinical
Lymphoma Myeloma and Leukemia 17(1); e34 2017.
[238] Ventz S, Alexander BM, Giovanni Parmigiani RDG, Trippa. L. Designing clinical trials that accept
new arms: An example in metastatic breast cancer. JCO May 22 2017.
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[239] Mazzola E, Coopey SB, Griffin M, Polubriaginof F, Buckley JM, Parmigiani G, Garber JE, Smith
BL, Gadd MA, Specht MC, Guidi A, Hughes KS. Reassessing risk models for atypical hyperplasia:
age may not matter. Breast Cancer Research and Treatment doi:10.1007/s10549-017-4320-7
2017.
[240] Samur MK, Gulla A, Fulciniti M, Samur AA, Szalat R, Shamas M, Magrangeas F, Minvielle S,
Anderson K, Parmigiani G, Avet-Loiseau H, Munshi N. Abstract 5719: Long intergenic non-
coding rnas: a new independent risk predictors in multiple myeloma. Cancer Research 77 (13
Supplement); 5719 2017.
[241] Ventz S, Cellamare M, Parmigiani G, Trippa L. Adding experimental arms to platform clinical
trials: randomization procedures and interim analyses. Biostatistics accepted 2017.
[242] Tyekucheva S, Bowden M, Bango C, Giunchi F, Huang Y, Zhou C, Bondi A, Lis R, Hemelrijck MV,
Andren O, Andersson SO, Watson RW, Pennington S, Finn S, Martin N, Stampfer M, Parmigiani
G, Penney K, Fiorentino M, Mucci L, Loda M. Stromal and epithelial transcriptional map of
initiation progression and metastatic potential of human prostate cancer. Nature Communications
accepted 2017.
[243] Tomasetti C, Durrett R, Kimmel M, Lambert A, Parmigiani G, Zauber A, Vogelstein B. Role of
stem-cell divisions in cancer risk. Nature 548(7666); E13–E14 2017.
[244] Wang V, Parmigiani G. Integrative factor analysis – an unsupervised method for quantifying
cross-study consistency of gene expression data. Genomics accepted 2017.
Book Chapters
[245] Parmigiani G, Polson NG.
Bayesian design for random walk barriers
. In: Bernardo JM, Berger
JO, Dawid AP, Smith AFM, eds., Bayesian Statistics 4. Proceedings of the Fourth Valencia
International Meeting, 715–721. Oxford: Oxford University Press 1992.
[246] Parmigiani G.
Scheduling inspections in reliability
. In: Basu AP, ed., Advances in Reliability, 303–
319. Amsterdam: Elsevier/North-Holland 1993.
[247] Parmigiani G, Kamlet M.
Cost-utility analysis of alternative strategies in screening for breast
cancer
. In: Gatsonis C, Hodges J, Kass RE, Singpurwalla N, eds., Case Studies in Bayesian
Statistics, 390–402. New York: Springer 1993.
[248] Parmigiani G, Berry DA.
Applications of Lindley information measure to the design of clinical
experiments
. In: Freeman PR, Smith AFM, eds., Aspects of Uncertainty. A Tribute to D. V.
Lindley, 351–362. Chichester: John Wiley & Sons 1994.
[249] M¨
uller P, Parmigiani G.
Simulation approach to one-stage and sequential optimal design problems
.
In: Kitsos C, Mueller W, eds., MODA 4 – Advances in Model Oriented Data Analysis, 37–48.
Springer 1995.
[250] Clyde M, M¨
uller P, Parmigiani G.
Optimal design for heart defibrillators
. In: Case studies in
Bayesian Statistics, Volume II (Lecture Notes in Statistics Vol. 105), 278–292. Springer-Verlag
Inc 1995.
[251] M¨
uller P, Parmigiani G.
Numerical evaluation of information theoretic measures
. In: Berry DA,
Chaloner KM, Geweke JK, eds., Bayesian Statistics and Econometrics: Essays in Honor of A.
Zellner, 397–406. New York: Wiley 1995.
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