Introduction to Comparative Effectiveness Course (HAP 823) Farrokh Alemi, Ph.D. This is an introduction to the course on comparative effectiveness, HAP 823.

Todays Agenda Introduction to the course We start with introduction to the course requirements.

Todays Agenda Introduction to the course History of causal analysis The we discuss history of causal analysis

Todays Agenda Introduction to the course History of causal analysis Access to data And end with arranging for access to the data and selection of teams.

Introduction Give your name What degree are you working on How well you know regression? Do you know SQL? You need to know this. Have you analyzed large data Are you linked to instructor’s LinkedIn page? If you are online, please use a Linked In page to describe yourself and post the URL for the page in the discussion area or email it to the instructor and other students.

I Don’t Know Operational Research Self taught SQL & statistics Analysis of massive data Readings from my articles My training was in operations research. Over the years I have taught myself about propensity scoring and related new statistical methods. I have designed the R program stratified covariate balancing. I have analyzed large datasets, some exceeding millions of cases. As we proceed with the course, you will see a number of my published articles. I should emphasize that this literature is dense and difficult to go through. While I have tried to simplify things and present just what you need to get by, I know that if you ask enough questions we will both get to an area that I do not know. So hopefully you will test the extent of my knowledge in this area.

Learn to Do Causal Analysis Overall Course Objective Learn to Do Causal Analysis The objective of this course is to help you do causal analysis. We do not cover the mathematical proofs or the theoretical issues. We take a very functional approach. Given a set of data can you analyze it using causal concepts.

Use any software you wish Applied Statistics Learn to Do Causal Analysis In doing the assignments in this course, you can use any software you wish. You can use SPSS, SAS, Netica, SQL, Minitab, Excel, or anything you wish. Do whatever that is convenient.

Objectives Propensity matching or weighting Covariate stratification & conditioning Counterfactual framework Reduce variables in data/stratification Present results, after and before balancing This course will focus on propensity scoring, covariate stratification, and conditioning. Since stratification of large data leads to computational difficulties, we discuss methods of partial matching and optimization of stratification. All content is taught through exercises that require data analysis.

Regression Analysis Prerequisites Before starting this course you are asked to have experience with multivariate regression analysis.

SQL is helpful Prerequisites Before starting this course you are asked to have experience with SQL analysis.

Review of Regression & Independence Course Topics We start with review.

Mediation Analysis & Case Control Study Design Course Topics Then we cover mediation analysis and stratified case control study design.

Bayesian networks & counterfactual framework Course Topics Then we introduce Bayesian networks and counterfactual framework

Propensity Scoring & Stratified Covariate Balancing Course Topics We end the first part of the course with propensity scoring and stratified covariate balancing.

Move Ahead with Your Career Why is this course important? This course is intended to help you move ahead with a career in data analysis, specially large data analysis.

Learn One, Do One, Teach One Course Paradigm The expectation is that you will learn to do a particular data analysis, you will then do so and teach it to others who are having difficult time with it.

Teams of Two, Never Same Two All Assignments Except Final Paper You are expected to work on assignments, except the final paper, in teams of two people.

Choose Your Teams Now -10% This is as good a time as any to choose who you will be working with. Keep in mind that if you do not have a partner you will lose 10% of grade in the assignment, except if there is prior approval. -10%

Prepare Narrated Slides For at least one topic, prepare narrated slides that can help others do the assignment in the section. Do this before the class when the lecture occurs. This is 20% of your grade 20%

Choose Topic Now Choose topic that you will narrate and provide guidance to other students.

Assignments Data Analysis 40% You are asked to do several data analysis after each lecture. 40%

Ok to Share Help Each Other Assignment grades are based on accuracy of analysis. Without copying other people’s work, it is ok to look over what they have done and how they have done it. You can look at the work of others to make sure you have the correct answers.

Ok to Do Assignments in Class Work in Class It is ok to work on assignments in class. In fact this is the preferred method. It is ok to contact the instructor each time you have a question. It is ok to text the instructor for faster responses.

Get Data Sign Up Today You can use data that is available on the web or get your own data from your organization or from an electronic health record 40%

Everyone can get A Grading There is no curve. Everyone can get an A.

Late Assignments -10% Grading Late data analysis assignments are accepted but grades are reduced by 10% each week it is late. This is done to encourage people to work together and be on the same page. Late narrations are not accepted. Late paper is accepted until one day prior to end of semester.

TxT Here is how you can contact me. Link with me on Linked IN. Use Twitter for public questions and answers. Text me at the phone number. Email me when you need.