Course Descriptions—Biostatistics

BSTT 400 - Biostatistics I (4 sh)

Descriptive statistics, basic probability concepts, one- and two-sample statistical inference, analysis of variance, and simple linear regression. Introduction to statistical data analysis software . Prerequisite: Enrollment restricted to public health students and healthcare administration students; other graduate, professional and advanced undergraduate students admitted by consent as space permits. To obtain consent, see the SPH registrar.

BSTT 401 - Biostatistics II (4 sh)

Simple and multiple linear regression, stepwise regression, multifactor analysis of variance and covariance, non-parametric methods, logistic regression, analysis of categorical data; extensive use of computer software. Prerequisite: BSTT 400.

BSTT 494 - Introductory Special Topics in Biostatistics (1 to 4 sh)

May be repeated for credit. Students may register for more than one section per term. Special topics in biostatistics. Content varies. Prerequisite: Consent of the instructor.

BSTT 505 - Logistic Regression and Survival Analysis (2 sh)

Interpretation of logistic regression and survival analysis models. Running logistic and proportional hazards regression models and constructing life-tables using SAS. Prerequisite: BSTT 401 and BSTT 401

BSTT 506 - Design of Clinical Trials (3 sh)

Rationale for clinical trials, blinding, ethical issues, methods of randomization, crossover trials, power and sample size calculations, data management, protocol deviation, data analysis, interim analysis. Prerequisites: BSTT 400 and BSTT 401

BSTT 507 - Sampling and Estimation Methods Applied to Public Health (3 sh)

The purpose of this course is to provide a comprehensive overview of current methods and issues in survey sample design and associated estimation procedures. Prerequisite: BSTT 401; or BSTT 502; or consent of the instructor

BSTT 523 - Biostatistics Methods I (4 sh)

Previously listed as BSTT 502. Foundations for and introduction to statistical inference, including one- and two-sample problems; regression analysis, including multiple regression and indicator variables. Prerequisites: College calculus, including multivariable calculus, concurrent registration in BSTT 524, and consent of the instructor.

BSTT 524 - Biostatistics Laboratory (2 sh)

Previously listed as BSTT 503. Use of spreadsheets for statistical investigations; use of statistical software; matrix theory, including methods relevant in biostatistical analysis. Prerequisites: Concurrent registration in BSTT 523 and consent of the instructor.

BSTT 525 - Biostatistics Methods II (4 sh)

Previously listed as BSTT 504.  Analysis of variance and multiple comparisons; model building and diagnostics; generalized linear models; logistic and Poisson regression; introduction to repeated measures and mixed models. Prerequisites: Grade of B or better in BSTT 523 and Grade of B or better in BSTT 524 or consent of the instructor.

BSTT 535 - Categorical Data Analysis (3 sh)

Previously listed as BSTT 511. Contingency tables and their tests, measures of association, stratified analysis, logistic regression, generalized linear model, Poisson regression, log-linear model, matched data, marginal homogeneity, ordinal data. Prerequisites: Grade of B or better in BSTT 525 and STAT 411, or consent of the instructor.

BSTT 536 - Survival Analysis (3 sh)

Previously listed as BSTT 512. Concepts of lifetime or survival distributions, especially with censored data; nonparametric estimation of the survival function; rank tests; proportional hazards regression models; parametric models. Prerequisites: Grade of B or better in BSTT 524 and Grade of B or better in STAT 411 or consent of the instructor.

BSTT 537 - Longitudinal Data Analysis (4 sh)

Previously listed as BSTT 513. Application and theory of models for longitudinal data analysis for both continuous and categorical response data, including use of statistical software for these methods. Prerequisites: Grade of B or better in BSTT 525 and Grade of B or better in STAT 411; or consent of the instructor.

BSTT 538 - Biostatistical Consulting (2 sh)

Previously listed as BSTT 514. Discussion of techniques required for successful biostatistical consultation; effective communication, problem formulation, data analysis, oral and written reports, supervised consulting experience. Prerequisites: Grade of B or better in BSTT 525; and consent of the instructor. Restricted to students enrolled in the biostatistics major.

BSTT 521 - Applied Multivariate Analysis (3 sh)

Previously listed as BSTT 580. Analysis of vector of responses; MANOVA, data reduction methods; introduction to cluster analysis, discriminant analysis, and structural equation models. Prerequisites: BSTT 513 and consent of the instructor.

BSTT 550 - Biostatistical Investigations (4 sh)

Previously listed as BSTT 522. Analysis of several large data sets that will require integration of numerous biostatistical tools; written summarization and discussion of results. Prerequisites: Grade of B or better in BSTT 535 and Grade of B or better in BSTT 536 and Grade of B or better in BSTT 537 and Grade of B or better in BSTT 538 and Grade of B or better or concurrent registration in BSTT 521.

BSTT 560 - Large Sample Theory (2 sh)

Previously listed as BSTT 534. Deriving and applying large sample statistical theories. The primary focus will be on limit theorems and their applications in biostatistical problems. Meets eight weeks of the semester. Prerequisites: Open only to PhD degree students; or consent of the instructor.  Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or mathematics.

BSTT 561 - Advanced Statistical Inference (3 sh)

Previously listed as BSTT 531. An in-depth consideration of some important ideas of statistical inference including large-sample theory, estimation, and testing. Specific topics to be covered include asymptotic theory, parameter estimation methods, and hypothesis testing. Some computer use in class. Prerequisites: Open only to PhD degree students; and consent of the instructor.

BSTT 562 - Linear Models (4 sh)

Previously listed as BSTT 533. Generalized inverse matrices; distributions for quadratic forms; estimability and testable hypotheses; constrained linear model; applications to regression, ANOVA, ANCOVA models; variance component models. Prerequisites:Open only to PhD degree students; or consent of the instructor.

BSTT 563 – Generalized Linear Models (4 sh)

Previously listed as BSTT 541. Students will learn the components of generalized linear models and their extensions. Prerequisites: BSTT 561 and concurrent registration in or prior completion of BSTT 560. Open only to PhD degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or methematics.

BSTT 564 – Missing Data (4 sh)

Previously listed as BSTT 542. Students will learn the statistical methods used for analyzing data with missing values. Prerequisites: BSTT 561 and concurrent registration in or prior completion of BSTT 560. Open only to PhD degree students; or consent of the instructor. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or methematics.

BSTT 565 – Computational Statistics (4 sh)

Previously listed as BSTT 543. Developing a broad and thorough working knowledge of modern statistical computing and computational statistics on a practical, conceptual, philosophical and mathematical level. Extensive computer use required. Prerequisites: Open only to PhD degree students; or consent of the instructor.  Concurrent registration in or prior completion of BSTT 560. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or mathematics.

BSTT 566 – Bayesian Methods (4 sh)

Previously listed as BSTT 544. Developing a broad and thorough working knowledge of Bayesian applications on a practical, conceptual, philosophical and mathematical level. Extensive computer use required. Prerequisites: Open only to PhD degree students; or consent of the instructor.  Concurrent registration in or prior completion of BSTT 560. Adequate training at level of intermediate mathematical statistics. Masters degree in biostatistics, mathematical statistics or mathematics.

BSTT 567 – Advanced Survival Analysis (4 sh)

Covers methods of analysis for multivariate survival data, including transition models and shared frailty models. Theory behind existing methodology is covered as well as implementation. Prerequisites: Grade of B or better or concurrent registration in BSTT 536; and consent of the instructor.

BSTT 594 – Special Topics in Biostatistics (1 to 4 sh)

May be repeated for credit. Students may register for more than one section per term. Advanced special topics. Content varies. Prerequisites: Consent of the instructor.

BSTT 595 - Biostatistics Research Seminar (1 sh)

Satisfactory/unsatisfactory grade only. May be repeated for credit. Current developments in theory and application of biostatistics and epidemiology with presentations by faculty and visiting scientists. Prerequisites: Consent of the instructor.