2  Course Description

PQHS 432 (cross-listed as CRSP 432 and MPHP 432) is the second half of a two-semester sequence (with 431) focused on modern data analysis and advanced statistical modeling, with a practical bent and as little theory as possible. We emphasize the key roles of thinking hard, and well, about design and analysis in research.

The course is formally titled Statistical Methods in Biological & Medical Sciences, Part 2. A more accurate title is Data Science for Biological, Medical or Health Research.

We’ll learn about managing and visualizing data, building models and making predictions, and other data science activities. This highly applied course focuses on modern tools for learning from data. We’ll learn a lot of R, and we’ll use RStudio and Quarto as tools to help make R work better, and help perform our research in rigorous and replicable ways.

2.1 Course Objectives

During the 431-432 sequence, students will:

  1. Use modern data science tools to import, tidy/manage, explore (through transformation, visualization and modeling) and communicate about data.
  2. Think hard and well about rigorous design and analysis in scientific research.
  3. Gain sufficient background in the practical issues regarding linear and generalized linear models to give you a starting place for meaningful applied work, particularly in terms of making comparisons to address general types of statistical and analytic questions (exploratory, predictive, inferential, and causal, in particular.)
  4. Learn about the importance of replicable research, and develop facility and practice in open source tools for doing it.
  5. Complete a series of assignments (labs, projects and quizzes) designed to help you demonstrate what you’ve learned.
  6. Program (“Code”) in R sufficiently to accomplish the tasks above, with enough self-sufficiency afterwards to be able to debug and use new R tools without substantial troubleshooting help. What separates “doing data science” from “doing data analysis” is programming.

This is NOT a course in mathematical statistics or statistical inference. It’s far more applied than that.

2.2 Key Topics in 431-432

  1. Exploratory Data Analysis: “All graphs are comparisons” including data exploration, statistical graphics and more general visualization of information.
  2. Placing biological, medical and health research questions into a statistical framework.
  3. Study Development - making choices in designing and executing the collection and aggregation of data.
  4. Data Handling - including important issues in importing, tidying and transforming data, as well as methods for dealing with missing data, including imputation.
  5. Statistical Comparisons: “All of statistics are comparisons” - including methods for discrete and continuous variables: intervals, assumptions, some thoughts on statistical power, and the bootstrap, design of visualizations and models for rates, proportions and contingency tables.
  6. The proper and rigorous use of multi-predictor models for continuous and discrete data, including…
    • Fitting, evaluating, and interpreting linear and generalized linear models.
    • Prediction and validation.
    • Critical role of graphics, including diagnostics and residual analysis.
    • Model choice, including variable selection, shrinkage and model uncertainty.
    • Dealing with categorical predictors and interactions meaningfully.
  7. Using R and RStudio to make all of the things above happen; with particular emphasis on doing replicable research and using Quarto) to document the work in a replicable way.

2.3 432 Course Outline & Format

The main group sessions for the 432 course will include over two dozen in-person lecture sessions led by Professor Love, to be held on Tuesdays and Thursdays from 1:00 to 2:15 PM in E321-323 in the Robbins Building at the CWRU School of Medicine.

  • The Course Calendar provides additional detail on specific sessions, and links to materials used in those sessions, including slides.