Course Introduction

Biostat 200C

1 Brief intro

1.1 Myself

1.1.1 Before 2021

• PhD in Biomathematics, UCLA (the department was renamed to “UCLA Computational Medicine”)
• Postdoc & Statistician, Harvard University
  • Department of Biostatistics
  • Channing’s Lab, Brigham and Women’s Hospital
• Assistant & Associate professor, University of Arizona (UofA)
  • Department of Epidemiology and Biostatistics
  • Statistics and Genetics Graduate Interdisciplinary Program (GIDP)
• Adjunct Associate Professor, UCLA
  • Department of Medicine Statistics Core (DOMStat)

1.1.2 2021 - Present

• Research principal investigator, Phoenix VA Health Care System
• Research principal investigator, Greater Los Angeles VA Health Care System
• Professor-in-Residence of Biostatistics, UCLA
  • Department of Biostatistics

1.2 TA

• Zian Zhuang
• Email: zianzhuang@ucla.edu

1.3 You?

2 Course webpages

• Github site: https://ucla-biostat-200c.github.io/2026spring/
  • slides, hw, announcements
• Burinlearn: https://bruinlearn.ucla.edu/courses/205219
  • announcements and hw submissions

3 What’s this course about?

• In 200B, we learn the linear models in the form \[ y = \beta_0 + \beta_1 x_1 + \cdots + \beta_p x_p + \epsilon, \] where
  1. \(y\) is a continuous response variable (or dependent variable),
     
  2. \(x_1, \ldots, x_p\) are covariates (or predictors, or independent variables), and
     
  3. \(\epsilon\) is the error term and assumed to be normally distributed and independent among observations.
• In 200C, we generalize the linear models in three directions.
  1. Generalized linear models (GLMs) handles nonnormal responses, \(y\).
     
     • binary response (disease or not)
     • proportions
     • counts
  2. Mixed effects models relaxes the independence assumption of the error term and allows correlation structure in \(\epsilon\).
     
     • Some data has a grouped, nested or hierarchical structure.
     • Repeated measures, longitudinal and multilevel data
  3. Nonparametric regression models (GAM, trees, neural networks) allow nonlinearity in the effects of predictors \(x\) on responses.

4 Course description

• Read syllabus and schedule for a tentative list of topics and course logistics.

• Teaching philosophy. Usually a GLM course is taught on blackboard/whiteboard with mostly math derivations. In this course, I will start from R code and then explain the theory behind it.