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List of course archives- 2012 - Econometrics with Observational Data
- 2011-2012 - Conducting Cost-Effectiveness Analyses with VA Data
- 2007 - Economic Analysis with Experimental Data
- 2005 Presentations
- 2003 Presentations
2012 - Econometrics with Observational Data
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April 4, 2012
Econometrics Course: Introduction and Identification
Todd Wagner, Ph.D. | Slides | Video- The objective of this class is to introduce participants to the course. We start by briefly describing a randomized trial and the leverage of experimental design to understand causation. We then transition into understanding causal pathways when experimentation isn’t possible. We introduce the concept of endogeneity and walk participants through the elements of an equation, as these equations often come up in other classes. Finally, we discuss the five main assumptions underlying the classic linear model, setting the stage for future classes.
- Statistical analysis of health care cost is made difficult by two data problems. Some patients incur disproportionate costs, a statistical property called skewness. Other patients incur no cost at all; the distribution is truncated. As a result of these problems, it is rarely a good idea to analyze cost using the classic linear statistical model, ordinary least squares (OLS). Transforming cost by the taking its log results in a variables this is more normally distributed, allowing use of an OLS regression. The parameters from this regression have a natural interpretation as the proportionate effect of a unit change in the independent variable on cost. Care must be used when predicting costs from a model based on the log of costs. Log models have other limitations. The most important of these is that they should not be used when there are many zero cost observations in the data.
- Health care cost can be difficult to analyze. In addition to skewness and truncation, the variance in cost data may be correlated with one of the predictor (independent) variables, a problem call heteroscedasticity. As a result of these problems, Ordinary Least Squares regression models may generate biased regression parameters and inaccurate predictions. Generalized Linear Models are an important alternative. A GLM includes a link function and a variance structure. These are identified using specific tests. Another alternative is a two-part model, which can be used to analyze data with many observations in which no cost was incurred. Non-parametric tests can be used to compare the cost incurred by two or more groups. Although they have the advantage of not requiring any assumptions about the statistical properties of the cost variable, they can be too conservative, and they do not allow the analyst to control for the effect of other factors.
- The OLS model is based on a continuous dependent variable. This lecture will introduce some of the methods available to treat other forms of dependent variables. Topics will include dichotomous (yes/no) outcomes, count data models, and choice models.
- Standard introductions to the OLS model pay limited attention to the right hand side variables. Several strong assumptions are made about the independent variables, including linearity and independence, that don’t always hold in health applications. This lecture will address some of the common problems with right hand side variables, and introduce methods to test for them, and methods to correct these problems. Issues to be addressed include non-linearity and functional form, multicollinearity, clustering, and robust standard errors.
- This lecture will briefly review the challenges of inferring causality in observational studies. We will cover several quasi-experimental methods to reduce the potential for bias in treatment effects using sample selection models, differences-in-differences, and regression discontinuity. Examples will be discussed for each method.
- Understanding causation with observational data is often more dependent on what we don’t observe than what we do observe. Multivariate techniques can be very useful for understanding observed characteristics. Propensity scores have emerged over the past 20 as another way to control for observables. We describe the concepts behind propensity scores and how they have been used (and misused) in practice. Finally, we work through an example using propensity scores.
- In observational studies, the assumptions of OLS may be violated for a variety of reasons and instrumental variables may be available to correct for these violations. In this lecture we will describe the concepts of instrumental variables techniques, review IVs used in VA research and provide a worked example.
- January 11, 2012
Modeling Health-Related Quality of Life over Time
Vilija Joyce, M.S. | Slides | Video
2011-2012 - Conducting Cost-Effectiveness Analyses with VA Data
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2007 - Economic Analysis with Experimental Data
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2005 Presentations
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2003 Presentations
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