The long-term goal of the proposed project is to develop flexible semi-parametric analysis tools for clinical, epidemiological and basic science studies in oncology. Semi-parametric models are potentially very useful in medical research because they involve both a parametric component which is usually easy to interpret and a nonparametric component which permits greater flexibility in the presence of biologic uncertainty. While these models are highly compelling scientifically, the limited availability of user-friendly inferential techniques for such models has severely restricted their use in practice. Part of the reason for this limitation is that the increased flexibility of the models adds an order of magnitude to the difficulty of the inference. Addressing this gap in inferential methodology is the central goal of the proposed research. This goal will be accomplished through achieving the following four aims: (1) Develop and evaluate more flexible and effective statistical analysis methods for transformation models in right censored time-to-event data; (2) Develop and evaluate tools for flexible semi-parametric risk-factor assessment in interval censored cancer studies; (3) Create tools for computationally efficient inference in semi-parametric models for cancer research; and (4) Develop flexible, semi-parametric methods for the analysis of extremely high dimensional screening data for cancer studies. While the application areas of these aims seems diverse, all of the aims involve a combination of semi-parametric model inference and empirical process methods. Thus the statistical underpinnings of the aims are highly interrelated. Relevance: Moreover, these aims will provide diverse cancer researchers with a significantly improved collection of scientifically meaningful, flexible, and user- friendly data analysis tools. ? ? ?
| Cao, Hongyuan; Kosorok, Michael R (2011) Simultaneous Critical Values For T-Tests In Very High Dimensions. Bernoulli (Andover) 17:347-394 |
| Zhao, Yufan; Zeng, Donglin; Socinski, Mark A et al. (2011) Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer. Biometrics 67:1422-33 |
| Nadkarni, Nivedita V; Zhao, Yingqi; Kosorok, Michael R (2011) Inverse regression estimation for censored data. J Am Stat Assoc 106:178-190 |
| Ma, Shuangge; Kosorok, Michael R (2010) Detection of gene pathways with predictive power for breast cancer prognosis. BMC Bioinformatics 11:1 |
| Kosorok, Michael R (2009) What's So Special About Semiparametric Methods? Sankhya Ser B 71-A:331-353 |
| Zhao, Yufan; Kosorok, Michael R; Zeng, Donglin (2009) Reinforcement learning design for cancer clinical trials. Stat Med 28:3294-315 |
| Song, Rui; Kosorok, Michael R; Fine, Jason P (2009) On Asymptotically Optimal Tests Under Loss of Identifiability in Semiparametric Models. Ann Stat 37:2409-2444 |
| Kosorok, Michael R (2009) Rejoinder on Discussion of: What's So Special About Semiparametric Methods? Sankhya Ser B 71-A:369-371 |
| Kosorok, Michael R (2009) On Brownian Distance Covariance and High Dimensional Data. Ann Appl Stat 3:1266-1269 |
| Ma, Shuangge; Kosorok, Michael R (2009) Identification of differential gene pathways with principal component analysis. Bioinformatics 25:882-9 |
Showing the most recent 10 out of 13 publications
