Health Services Research Methods I (HSMP 7607)

This is the new version my Health Services Methods I class (starting in the 2020 Fall semester). This class is an introduction to regression models (linear, logistic, probit, GLMs) and research designs for observational data (regression adjustment/propensity scores, difference-in-difference, regression discontinuity, instrumental variables, longitudinal data). We will practice model interpretation until it becomes (almost) second nature. The old version of this class was an introduction to regression modeling.

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Syllabus (2022)

Lecture notes [I'll update the notes on Fall 2022]

L1: Overview of the class

L2: Introduction to Stata and review of linear/OLS regression

L3: Causal inference

L4: Applied review of regression

L5: Regression adjustment and propensity scores (intro)

L6: Difference-in-difference models (intro)

L7: Regression discontinuity (intro)

L8: Maximum likelihood estimation (MLE)

L9: Marginal effects to interpret models

L11: GLM models and analysis of cost data

L12: Propensity scores and matching estimators

L13: Difference-in-difference designs (estimation)

L14: Regression discontinuity designs: rdrobust, sharp and fuzzy RDD, and intro to instrumental variables. This has a comparison of parametric with nonparametric rdrobust, which, given a bandidth h, it's really a kernel-weighted parametric method (see slides 59 and 60). The data-driven optimal bandwidth is the more interesting part.


© Marcelo Coca Perraillon, 2021. No part of the materials available through the site may be copied, photocopied, reproduced, translated or reduced to any electronic medium or machine-readable form, in whole or in part, without prior written consent of the author. Any other reproduction in any form without the permission of the author is prohibited. All materials contained on this site are protected by United States copyright law and may not be reproduced, distributed, transmitted, displayed, published or broadcast without the prior written permission of author.