Courses Taught by Lu Wang

BIOSTAT601: Probability and Distribution Theory

  • Graduate level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Wang, Lu
  • Prerequisites: Three terms of calculus
  • Description: Fundamental probability and distribution theory needed for statistical inference. Probability, discrete and continuous distributions, expectation, generating functions, limit theorems, transformations, sampling theory.
  • Syllabus for BIOSTAT601
Concentration Competencies that BIOSTAT601 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
BIOSTAT MPH Apply the theoretical foundations of probability theory and distribution theory BIOSTAT601
BIOSTAT MS Apply the theoretical foundations of probability theory and distribution theory BIOSTAT601

BIOSTAT881: Causal Inference and Statistical Methods for Personalized Health Care

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Wang, Lu
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 651, BIOSTAT 653, BIOSTAT 801, and BIOSTAT 802 (concurrent also accepted).
  • Description: This course discusses statistical theory/methodology aimed at addressing causal inquiries from observational data and complex randomized designs, as well as statistical methods for evaluating dynamic treatment regimes for personalized health care. Two key approaches will be focused on: the directed acyclic graph (DAG) and models for counterfactuals (structural models).
  • Learning Objectives: At the end of the course the students will be able to: 1) Formulate causal contrasts of interest for addressing specific scientific inquiries. 2) Derive graphical models for investigating the conditions under which the causal contrasts of interest are identified from data collected under specific study designs. 3) Formulate adequate structural models for making inference about the causal contrasts of interest. 4) Understand the statistical formulation and methods for evaluating dynamic treatment regimes.