Advanced Statistical Methods for Observational Studies

Lecture Mondays 2:30-4:20pm. Class meets online: (welcome audio)

To see full course materials from Spring 2019 go here

Mike Baiocchi MSOB x318, baiocchi{AT}stanford{DOT}edu by appointment

David Rogosa Sequoia Hall 224, rag{AT}stanford{DOT}edu

Course web page: http://rogosateaching.com/somgen290/

From explorecourses

STATS 266: Advanced Statistical Methods for Observational Studies (EDUC 260B, HRP 292, CHPR 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 http://rogosateaching.com/somgen290/ Terms: Spr | Units: 2-3 | Grading: Medical Option (Med-Ltr-CR/NC)

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 Week 10 - Mendelian randomizations and synthetic cohorts

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

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

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 3.6.3 (Holding the Windsock) released on 2020-02-29.

For references and software: The R Project for Statistical Computing

Closest download mirror is Berkeley If Berkeley is offline, choose a mirror from the main R page (first link).

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.

online Verzani book resources: version of text available from John Verzani's page . alternate version, single pdf UsingR R-package

The Registrar does have a form (no-fee) for faculty, staff, post-docs: Application for Auditor or Permit to Attend (PTA) Status