Biostatistics (BSTA)

BSTA 509 Introduction to Epidemiology

Taught by: Yu-Xiao Yang

Also Offered As: EPID 801

Activity: Lecture

0.5 Course Units

BSTA 511 Biostatistics in Practice

Taught by: Russell T. Shinohara

Course usually offered in fall term

Prerequisites: Open to Biostatistics student only.

Activity: Lecture

0.5 Course Units

BSTA 550 App Reg and Anal of Var

Also Offered As: PSYC 611, STAT 500

Activity: Lecture

1 Course Unit

BSTA 620 Probability I

This course covers Elements of (non-measure theoretic) probability necessary for the further study of statistics and biostatistics. Topics include set theory, axioms of probability, counting arguments, conditional probability, random variables and distributions, expectations, generating functions, families of distributions, joint and marginal distributions, hierarchical models, covariance and correlation, random sampling, sampling properties of statistics, modes of convergence, and random number generation.

Course usually offered in fall term

Prerequisites: Two semesters of calculus (through multivariable calculus), linear algebra; permission from the instructor.

Activity: Lecture

1 Course Unit

BSTA 621 Statisical Inference I

This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

Course usually offered in spring term

Prerequisites: BSTA 620, permission of instructor.

Activity: Lecture

1 Course Unit

BSTA 622 Statistical Inference II

This This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

Course usually offered in fall term

Prerequisite: BSTA 621; permission of instructor

Activity: Lecture

1 Course Unit

BSTA 630 Statistical Methods and Data Analysis I

This first course in statistical methods for data analysis is aimed at first-year Biostatistics students. It focuses on the analysis of continuous data. Topics include descriptive statistics (measures of central tendency and dispersion, shapes of distributions, graphical representations of distributions, transformations, and testing for goodness of fit); populations and sampling (hypotheses of differences and equivalence, statistical errors); one- and two-sample t tests; analysis of variance; correlation; nonparametric tests on means and correlations; estimation (confidence intervals and robust methods); categorical data analysis (proportions; statistics and test for comparing proportions; test for matched samples; study design); and regression modeling (simple linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicollinearity). Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

Course usually offered in fall term

Prerequisites: Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently) and permission of instructor.

Activity: Lecture

1 Course Unit

BSTA 632 Statistical Methods for Categorical and Survival Data

This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

Course usually offered in spring term

Prerequisites: BSTA 630, 620, 621 (may be taken concurrently),linear algebra, calculus and permission of instructor.

Activity: Lecture

1 Course Unit

BSTA 651 Introduction to Linear Models and Generalized Linear Models

This course extends the content on linear models in BSTA 630 and BSTA 632 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance, general linear hypothesis, estimability, polynomial, piecewise, ridge, and weighted regression, regression and collinearity diagnostics, multiple comparisons, fitting strategies, simple experimental designs (block designs, split plot), random effects models, Best Linear Unbiased Prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example data sets will be emphasized.

Course usually offered in spring term

Prerequisites: Linear algebra, calculus, BSTA 620, 630. BSTA 621 and 632 (may be taken concurrently), permission of instructor.

Activity: Lecture

1 Course Unit

BSTA 690 Consulting Laboratory I

Participation in the consulting laboratory is a requirement for both the Master's and Ph.D. degrees. This course covers general principles of statistical consulting and statistical consulting experience. There is training on statistical programming, preparation of reports, presentations, and the communication aspects of consulting. Each student will be expected to join one of several project teams consisting of faculty, research staff, and graduate student consultants; attend meetings along with the project team and associated investigators; participate in all or part of the design, management, analysis and reporting stages of a project; and gain valuable experience in working with actual research projects.

Taught by: Faculty

One-term course offered either term

Prerequisite: BSTA 630

Activity: Lecture

1 Course Unit

BSTA 752 Categorical Data Analysis II

Activity: Lecture

1 Course Unit

BSTA 754 Advanced Survival Analysis

This advanced survival analysis course will cover statistical theory in counting processes, large sample theory using martingales, and other state of the art theoretical concepts useful in modern survival analysis research. Examples in deriving rank-based tests and Cox regression models as well as their asymptotic properties will be demonstrated using these theoretical concepts. Additional potential topics may include competing risk, recurrent event analysis, multivariate failure time analysis, joint modeling of survival and longitudinal data, sample size calculations, multistate models, and complex sampling schemes involving failure time data.

Course usually offered in fall term

Prerequisites: BTA 622 (may be taken concurrently) and permission of instructor.

Activity: Lecture

1 Course Unit

BSTA 774 Statistical Methods for Evaluating Diagnostic Tests

This course will cover statistical methodology for evaluating diagnostic tests.The topics will include: estimation of ROC curves, comparing multiple diagnostic tests, developing diagnostic tests using predictive models, measurement error effects on diagnostic tests, random effects models for multi-reader studies, verification bias in disease classification, methods for time-dependent disease classifications, study design issues, related software, and meta-analyses for diagnostic test data.

Course usually offered in fall term

Prerequisites: BSTA 510, BSTA 630, BSTA 631 or equivalent; permission of instructor

Activity: Lecture

1 Course Unit

BSTA 820 Statistical Inference III

Statistical inference including estimation, confidence intervals, hypothesis tests and non-parametric methods.

Taught by: Faculty

Course usually offered in spring term

Also Offered As: STAT 972

Prerequisites: To be advised.

Activity: Lecture

1 Course Unit

BSTA 899 Pre-Dissertation Lab Rot

Activity: Lecture

1 Course Unit