CHPR 290 (also Statistics 266 , Education 260B) Spring 2016
Advanced Statistical Methods for Observational Studies
CHPR 290: Lecture Mondays 2:30-4:20pm. Class meets in HRP T138B.
New Location McCullough 122 (476 Lomita Mall, up from Sequoia towards Engineering)
Instructors
Mike Baiocchi MSOB x318, baiocchi{AT}stanford{DOT}edu Thursday 9 - 11 AM
David Rogosa Sequoia Hall 224, rag{AT}stanford{DOT}edu Monday 4:30 - 5:30
Course web page: http://rogosateaching.com/somgen290/
From explorecourses CHPR 290: Advanced Statistical Methods for Observational Studies (EDUC 260B, STATS 266)
Design principles and statistical methods for observational studies, particularly for cause and effect determinations.
Topics include: matching methods, sensitivity analysis, instrumental variables, graphical models, marginal structural models.
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 http://rogosateaching.com/somgen290/
Terms: Spr | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)
Abbreviated Course Outline
Week 1 - Introduction, Potential Outcomes and Study Design.
Week 2 - Causal Inference in Randomized Experiments and Models for Observational Studies
Week 3 - Matching Methods and Implementations
Week 4 - Further issues: Design Sensitivity
Week 5 - Further issues: Planning Analysis
Week 6 - Instrumental Variable Methods.
a. encouragement designs and compliance adjustments
b. fixing broken regressions: (IV illusion or promise)?
Week 7 - Natural Experiments: Regression Discontinuity, Interrupted Time Series, Difference in Differences
Week 8 - The Role of Graphical models, DAGs
Week 9 - Applications of Marginal Structural Models.
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, directed acyclic graphs, and marginal structural models. 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
Textbooks
Required
Design of Observational Studies, Paul Rosenbaum, 1st Edition (Springer)
Available online: Stanford access
Additional Resources
Causal Inference, Miguel Hernan & Jamie Robins
Available online: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Causal Inference in Statistics, Social and Biomedical Sciences: An Introduction, Guido Imbens and Don Rubin, 1st Edition (Cambridge University Press) Stanford access
Computation
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 for spring quarter will be R version 3.2.4 to be released on 2016-03-10. 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: Suggested problems will be posted each week. Solutions will be posted at approximately the same time. These problems are for your own use 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.
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. With our new room assignment we now have 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