Biostatistics (BIOSTAT)

Instructor(s): Ali MirzazadehAli Mirzazadeh also teaches: BIOSTAT 212, GLOBL HLTH 207, EPIDEMIOL 253, GLOBL HLTH 217

Instructor(s): Ali Mirzazadeh

Course is an introduction to the study of biostatistics. Course addresses types of data, their summarization, exploration and explanation, as well as concepts of probability and their role in explaining uncertainty. Course concludes with coverage of inference applied to means, proportions, regression coefficients and contingency tables. Throughout the course, the software program STATA will be used.

Instructor(s): Aaron Scheffler

Instructor(s): Aaron Scheffler

Instruction in multiple predictor analyses as a tool for control of confounding and for constructing predictive models. Topics will include exploratory data analyses, linear regression, and logistic regression. The STATA statistical package will be used.

Instructor(s): Chiung-Yu Huang

Instructor(s): Chiung-Yu Huang

Advanced instruction in multiple predictor analyses. Topics will include survival analysis and regression for repeated measures. In the final weeks of the course, participants will receive individualized instruction for the analysis of their own data.

Instructor(s): Dave GliddenDave Glidden also teaches: EPIDEMIOL 270, EPIDEMIOL 299D

Instructor(s): Dave Glidden

This is a continuation of the Biostatistical Methods in Clinical Research series, covering additional methods in multi-predictor analyses and allowing more in-depth exploration of the topics covered in Biostat I (BIOSTAT 183), II (BIOSTAT 208) and III (BIOSTAT 209). Topics in survival analysis and longitudinal analysis will be emphasized and students are also encouraged to utilize their own projects to motivate discussion and to suggest topics of interest.

Instructor(s): Fei Jiang

Instructor(s): Fei Jiang

The goal of this course is to equip students with core statistical concepts and methods. In this course students will learn mathematical, computational, statistical and probabilistic background; the basics of probability distributions including the definitions of density functions, cumulative distributions, moments of the distributions; theory and methods for point estimation; and methodology for the construction of hypothesis testing and confidence intervals. R statistical software will be used

Instructor(s): Ali MirzazadehAli Mirzazadeh also teaches: BIOSTAT 200, GLOBL HLTH 207, EPIDEMIOL 253, GLOBL HLTH 217

Instructor(s): Ali Mirzazadeh

This course will introduce clinical researchers to the use of computer software for managing and analyzing clinical research data. Currently available statistical packages will be described and the roles of spreadsheet and relational database programs discussed. Use of STATA for managing, cleaning, describing, and analyzing data will be taught in lecture and laboratory sessions.

Instructor(s): Fan Xia, Thomas NewmanThomas Newman also teaches: EPIDEMIOL 204, EPIDEMIOL 221

Instructor(s): Fan Xia, Thomas Newman

The course will define causal effects in terms of potential outcomes, show when standard regression methods do and do not support causal inferences, and show how to estimate and interpret marginal and conditional causal effects. It will also cover propensity scores, inverse probability weighting, marginal structural models (for time-dependent treatments with time-dependent confounder/mediators), mediation analysis, new-user designs, instrumental variables, and principal stratification.

Instructor(s): Patrick PhillipsPatrick Phillips also teaches: EPIDEMIOL 205, Suzanne Dufault

Instructor(s): Patrick Phillips, Suzanne Dufault

This course provides a foundation in modern biostatistical methods and statistical reasoning for pharmaceutical sciences research. The course will explore common data types and distributions, experimental design, exploratory data analysis, methods for hypothesis testing (both parametric and non-parametric), and model-building and comparison. During this hands-on course, students will reinforce their understanding by implementing what they have learned in R.

Instructor(s): David Quigley

Instructor(s): David Quigley

This course provides an introduction to biostatistical methods. The course emphasizes practical considerations required to design studies, perform elementary analysis, and become an informed consumer of statistical data. Topics include study design, exploratory data analysis, the P value and hypothesis testing, power analysis, and reproducible analysis methods using the R statistical environment. This course will emphasize applications in favor of mathematical detail.