The goal of the Biostatistics Core Is to collaborate with SPORE investigators and other core resource scientists to enhance the quality of the research undertaken in the University of Michigan Prostate SPORE. The Core personnel have been chosen because of their expertise in relevant areas of Biostatistics and Bioinformatics that is specifically required for the SPORE projects to succeed. Support will be provided in all stages of the research, beginning with the formulation of the research question, through the experimental design stage and data collection stage, to data analysis and interpretation, to the writing of reports and dissemination of results. It will be apparent from this proposal that Core personnel have played a significant role in designing the proposed experiments and in planning the data analysis. The exact nature of the collaboration will depend on the specifics of the science and the needs of the project. In addition to direct support of the projects and other cores, senior statisticians will also focus on statistical methodology development related to the needs of prostate cancer research in this SPORE. Thus the Specific Aims of the Core are: 1) Assist investigators in the design of clinical and laboratory experiments; 2) Assist investigators in the analysis and interpretation of data from clinical and laboratory experiments and in writing of manuscripts relaying prostate cancer SPORE results to the scientific community; 3) Undertake translational biostatistics research to develop methodology and software implementation relevant to prostate cancer.
The Biostatistics Core is critical to the ongoing success of the UM Prostate SPORE. It has contributed to the success of the current projects and has participated in a meaningful way to the planning and design of the proposed projects. Our ultimate goal is to decrease the morbidity and mortality of prostate cancer through innovative research that is supported by rigorous biostatistical design and support.
|Zhang, Yajia; Pitchiaya, Sethuramasundaram; Cie?lik, Marcin et al. (2018) Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression. Nat Genet 50:814-824|
|Hussain, Maha; Daignault-Newton, Stephanie; Twardowski, Przemyslaw W et al. (2018) Targeting Androgen Receptor and DNA Repair in Metastatic Castration-Resistant Prostate Cancer: Results From NCI 9012. J Clin Oncol 36:991-999|
|Salami, Simpa S; Hovelson, Daniel H; Kaplan, Jeremy B et al. (2018) Transcriptomic heterogeneity in multifocal prostate cancer. JCI Insight 3:|
|Zhao, Shanshan; Leonardson, Amy; Geybels, Milan S et al. (2018) A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer. Prostate :|
|Niknafs, Yashar S; Pandian, Balaji; Gajjar, Tilak et al. (2018) MiPanda: A Resource for Analyzing and Visualizing Next-Generation Sequencing Transcriptomics Data. Neoplasia 20:1144-1149|
|Xiao, Lanbo; Tien, Jean C; Vo, Josh et al. (2018) Epigenetic Reprogramming with Antisense Oligonucleotides Enhances the Effectiveness of Androgen Receptor Inhibition in Castration-Resistant Prostate Cancer. Cancer Res 78:5731-5740|
|Piert, Morand; Shankar, Prasad R; Montgomery, Jeffrey et al. (2018) Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications. EJNMMI Res 8:23|
|Wu, Yi-Mi; Cie?lik, Marcin; Lonigro, Robert J et al. (2018) Inactivation of CDK12 Delineates a Distinct Immunogenic Class of Advanced Prostate Cancer. Cell 173:1770-1782.e14|
|Rice, John D; Tsodikov, Alex (2017) Semiparametric profile likelihood estimation for continuous outcomes with excess zeros in a random-threshold damage-resistance model. Stat Med 36:1924-1935|
|Shen, Rex; Luo, Lan; Jiang, Hui (2017) Identification of gene pairs through penalized regression subject to constraints. BMC Bioinformatics 18:466|
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