HS 852, or equivalent, instructor may review syllabi of previously taken courses (past 5 years) and/or require a test to assess the equivalence of the student background, as necessary.

Course Description

This course will cover a number of modern analytical methods for advanced healthcare research. Specific focus will be on reviewing and using innovative modeling, computational, analytic and visualization techniques to address specific driving biomedical and healthcare applications. The course will cover the 5 dimensions of Big-Data (volume, complexity, time/scale, source and management). HS853 is a 4 credit hour course (3 lectures + 1 lab/discussion).


Students will learn how to:
  • Research, employ and report on recent advanced health sciences analytical methods
  • Read, comprehend and present recent reports of innovative scientific methods applicable to a broad range of health problems
  • Experiment with real Big-Data.

Examples of Topics Covered

  • Foundations of R
  • Scientific Visualization
  • Review of Multivariate and Mixed Linear Models
  • Causality/Causal Inference and Structural Equation Models
  • Generalized Estimating Equations
  • Dimension reduction
  • Instrument reliability (Cronback’s α)
  • PCOR/CER methods Heterogeneity of Treatment Effects
  • Big-Data / Big-Science
  • Scientific Validation: Internal statistical cross-validaiton
  • Missing data
  • Genotype-Environment-Phenotype associations
  • Variable selection (regularized regression and controlled/knockoff filtering)
  • Medical imaging
  • Non-parametric inference
  • Machine learning prediction, classificaiton, and clustering
  • Databases/registries
  • Meta-analyses
  • Classification methods
  • Longitudinal data and time-series analysis
  • Geographic Information Systems (GIS)
  • Psychometrics and Rasch measurement model analysis
  • MCMC sampling for Bayesian inference
  • Network Analysis

Teaching and Learning Methods

This course meets weekly four times on campus however, as necessary, blended instructional techniques will be employed to accommodate student and program constrains. Synchronous web-streaming of lectures/labs and asynchronous virtual office hour forums will be supported. Assignments will be announced on the web and will be electronically collected, graded and recorded. A variety of teaching methods will be used including lecture, Journal Club, discussion, small group work, and guest presentationn.


Scientific Methods for Health Sciences EBook. Additional resources will be made available through the SOCR Wiki and may include chapters, websites for review, references, reports posted online, ebooks and learning modules.

Assignments and Evaluation Methods

  • 40% Homework Projects
  • 30% Midterm Exam
  • 30% Final Paper

Current Offerings

Past Offerings

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