The University of Washington, Department of Biostatistics, together with faculty from the Departments of Epidemiology, Medicine and Statistics, proposes to train biostatisticians for collaborative work in HIV/AIDS research. This renewal of a very successful training grant would support 4 predoctoral trainees. The PhD program includes coursework in statistical theory, biostatistical methodology, and data analysis, and a statistical consulting practicum. Coursework in the biological sciences related to HIV/AIDS, and an oral examination on a topic in HIV/AIDS biology or medicine, are required. After satisfying these requirements the student proceeds to complete a doctoral dissertation. The research component of the training program will focus on collaboration in ongoing biostatistical methodology research and significant involvement in HIV/AIDS clinical research projects. All trainees are additionally required to attend and contribute to ongoing seminar series that provide collaborative HIV/AIDS research opportunities. Students are enrolled with prior training in mathematics, statistics, or the biological sciences. Program prerequisites include linear algebra, probability, and two years of calculus;promising students with biology majors may be admitted provisionally if they have not satisfied all the mathematical prerequisites.
|Odem-Davis, Katherine; Fleming, Thomas R (2013) Adjusting for unknown bias in non-inferiority clinical trials. Stat Biopharm Res 5:|
|Fleming, Thomas R; Odem-Davis, Katherine; Rothmann, Mark D et al. (2011) Some essential considerations in the design and conduct of non-inferiority trials. Clin Trials 8:432-9|
|Renner, Brandon; Strassheim, Derek; Amura, Claudia R et al. (2010) B cell subsets contribute to renal injury and renal protection after ischemia/reperfusion. J Immunol 185:4393-400|
|Moskowitz, Chaya S; Pepe, Margaret S (2004) Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome. Stat Med 23:1555-70|
|Moskowitz, Chaya S; Pepe, Margaret S (2004) Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes. Biostatistics 5:113-27|