CHPR 266 (also Statistics 266 , HRP 292, Education 260B)   Spring 2019
    Advanced Statistical Methods for Observational Studies

CHPR 266:   Lecture Mondays 2:30-4:20pm.  Class meets in MSOB X303
    Medical School Office Building (MSOB), 1265 Welch Road.

                To see full course materials from Spring 2018 go here
Mike Baiocchi     MSOB x318,   baiocchi{AT}stanford{DOT}edu   by appointment
David Rogosa   Sequoia Hall 224,   rag{AT}stanford{DOT}edu   Monday 4:45 - 5:30
Course web page:

From explorecourses
  CHPR 266: Advanced Statistical Methods for Observational Studies (EDUC 260B, HRP 292, STATS 266)
Design principles and statistical methods for observational studies, particularly for cause and effect determinations. 
Topics include: matching methods, sensitivity analysis, instrumental variables. 
3 unit registration requires a small project and presentation. Computing is in R. 
Pre-requisites: HRP 261 and 262 or STAT 209 ( HRP 239), or equivalent. See
Terms: Spr | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

Abbreviated Course Outline
Week 1 - Course Introduction; Matching Methods Part 1 (intro and theory) 
Week 2 - Matching Methods Part 2 (implementation); Potential Outcomes and Study Design 
Week 3 - Full matching, Inclusion and Exclusion, and Defining Treatment Effects
Week 4 - Models for Observational Studies and Inverse Probability Weighting
Week 5 - Randomized Experiments and Design Sensitivity
Week 6 - Augmenting the Primary Study: Second Outcomes, Known Nulls, Coherence, 
          Multiple Contrast Groups, and Thick Description 
Week 7 - Alternative Designs: Discontinuity Designs and Case-Noncase Studies 
Week 8 - RCT designs with Instrumental Variables
Week 9 - Observational Studies with Instrumental Variables 
Course Readings, Files and Examples

Class Calendar

In this course students will:
(1) Learn to identify key statistical issues in observational studies and methods and study designs to address issues of confounding.
(2) Become proficient with advanced statistical methods for observational studies: methods for missing data, matching based inference, sensitivity analysis, propensity score methods, instrumental variables. You should know which methods are useful in different situations, and which conditions have to be checked for the method to be applicable.
(3) Be able to perform detailed data analyses on a variety data using the statistical computation environment R. You should be able to implement all the methods presented in this course.
Prerequisites: HRP261 and 262, or Stat 209 (HRP 239) or by permission of instructor

Design of Observational Studies, Paul Rosenbaum, 1st Edition (Springer) Available online:   Stanford access
Additional Resources
Causal Inference, Miguel Hernan & Jamie Robins Available online:
Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction, Guido Imbens and Don Rubin, 1st Edition (Cambridge University Press)   Stanford access

We will make extensive use of the statistical computation and programming environment R. It is free and open source, and it has become the de facto standard for statistical analyses in most areas of academia and industry. Current version of R is R version 3.5.3 released on 2019-03-11 -- "Great Truth". For references and software: The R Project for Statistical Computing   Closest download mirror is Berkeley  The CRAN Task Views provide an organization and overview of the many R packages.
One specialized resource this course will draw from is Paul Rosenbaum's software page
Many students may also use or be interested in an editor for R (beyond emacs or command line). Professor Baiocchi will be using RStudio, download at .
   Basic R References.
Using R for Introductory Statistics, Verzani, 1st Edition (Chapman & Hall/CRC)
       online Verzani book resources:   version of text available from John Verzani's page .   alternate version, single pdf    UsingR R-package
Introductory Statistics with R, Dalgaard, 2nd Edition (Springer); also see ISwR package for data and functions
A handbook of statistical analyses using R (second edition). Brian Everitt, Torsten Hothorn CRC Press, Index of book chapters   Stanford access      Data sets etc Package 'HSAUR2'

Course Components, Student Work
Homeworks: Review Questions will be posted each week. Solutions will be posted at approximately the same time. These problems are for your own learning and will not be collected or graded.
Problem Sets. During the Spring Quarter there will be two, take-home problem sets. TH1 covers weeks 1 - 5; TH2 covers weeks 6 - 9; see class calendar. These problem sets will be the basis for grading the two-unit enrollment. No collaboration or external assistance is permitted.   
Problem Set 1
Problem Set 2

Third unit enrollment, Project/Presentation. If you are enrolled for three units credit, the additional unit requirement to complete a small project and give a short presentation (approximately 15 minutes with handout). These presentations will be scheduled in the last two weeks of the quarter.

Note to auditors. Room assignment should allow space for auditors.
The Registrar does have a form (no-fee) for faculty, staff, post-docs: Application for Auditor or Permit to Attend (PTA) Status