Course Offerings

Course Fall 2017 Winter 2018 Spring 2018
FMPH 221A. Schwartzman  
FMPH 222 K. Messer 
FMPH 223  F. Vaida
FMPH 224  S. Jain
FMPH 226 X. Tu 
FMPH 227L. Natarajan  
FMPH 241Various FacultyVarious FacultyVarious Faculty
FMPH 290W. ThompsonW. ThompsonW. Thompson
Math 284  R. Xu

Course Schedule

*Please note that this schedule is subject to change
**For the most current quarter you can also check the
Schedule of Classes

Fall 2017 Days Time
FMPH 221Tu/Th2:30-4:00 pm
FMPH 227Tu/Th9-10:30 am
FMPH 290Tu11:30-1:30 pm
Winter 2018  
FMPH 222*M/W9:00-10:30 am
FMPH 226*Tu/Th2:30-4:00 pm
FMPH 290*Tu11:30-1:30 pm
Spring 2018  
FMPH 223*W/F9:00-10:30 am
FMPH 224*Tu/Th1:30-3:00 pm
FMPH 290*Tu11:30-1:30 pm
Math 284*M/W/F2:00-3:00 pm

Course Descriptions

FMPH 221. Biostatistical Methods I (4)

Prerequisites: Biostatistics major or program/instructor approval

Introductory graduate course on the analysis of biomedical data using the R statistical software.  Topics include t-tests, ANOVAs, linear regression, model diagnostics, model building and selection, interaction, confounding, multiple comparisons, and robust tests based on ranks and resampling.

FMPH 222. Biostatistical Methods II (4)

Prerequisites: Successful completion of FMPH 221 and (Math 281A or Math 282A) or program/instructor approval.

Intermediate-level graduate course in the analysis of categorical data.  Topics include generalized linear models (logistic, Poisson, loglinear models); splines and nonlinear regression; stratified and case-control studies.  Maximum likelihood, quasi likelihood and Bayesian approaches; large scale model selection and inference.

FMPH 223. Analysis of Longitudinal Data (4)

Prerequisites: Successful completion of FMPH 222 and (Math 281A and Math 282A or Math 281B and Math 282B) or program/instructor approval.

Covers analysis of longitudinal data, parametric modeling of covariance, generalized estimating equations, linear, nonlinear and generalized linear mixed effects models and modeling dropout in longitudinal studies.  Data analysis and computational issues are emphasized.

FMPH 224. Clinical Trials (4)

Prerequisites: Successful completion of FMPH 221 and FMPH 222 or program/instructor approval.

Graduate class will cover statistical aspects of clinical trial design, monitoring, analysis and ethics of human subjects research.  Data analysis and computation will be emphasized.

FMPH 226. Statistical Methods for Observational Studies (4)

Prerequisites: Successful completion of FMPH 221, FMPH 222, and FMPH 223 or program/instructor approval.

Graduate class is an introduction to inference and causal modeling for observational data, including propensity score adjustment, inverse probability weighting, instrumental variables, and sensitivity analysis.  Data analysis and computation will be required.

FMPH 227. Applied Multivariate Statistical Analysis (4)

Prerequisites: Successful completion of FMPH 221 and FMPH 222 or program/instructor approval.

Graduate course covers concepts, methods and applications of multivariate data analysis, including multivariate regression, principal components, clustering and functional data analysis.  Data analysis will be emphasized.

FMPH 241. Biostatistics Rotation (3)

Prerequisites: Biostatistics major only.

This practicum provides hand-on experience with biomedical research and data analysis.  Working within a specific biomedical domain (e.g., cancer, genomics, or physical activity research), students will conduct original data analysis, and prepare or substantially contribute to final project report.

FMPH 290. Biostatistics Journal Club and Seminar (1)

Prerequisites: Biostatistics major only.

This course requires attendance and participation in Division of Biostatistics seminar series and journal club.  Students will critically read the assigned articles and participate in biweekly journal clubs.  Students are also required to lead at least one journal club discussion.

MATH 284. Survival Analysis (4)

Prerequisites: Math 282A or instructor approval

Survival analysis is an important tool in many areas of applications including biomedicine, economics, and engineering. It deals with the analysis of time to events data with censoring. This course discusses the concepts and theories associated with survival data and censoring, comparing survival distributions, proportional hazards regression, nonparametric tests, competing risk models, and frailty models. The emphasis is on semiparametric inference, and material is drawn from recent literature.