Unbiased Analysis of Today's Healthcare Issues
What does fixed effects and random effects mean? For economists, fixed effects means The fixed effects model is a linear regression of y on x, that adds to the speci cation a series of indicator variables. For example, one could include a series of country dummies in comparative time series cross-sectional data to account for unexplained…
Consider the case where you have observations on the IQ of six individuals. Let say that three of the individuals are from California and three are from Florida. Assume the following data structure: California: 90, 110, 130 Florida: 95, 100, 120 In this case, the mean IQ nationally is 107.5, the variance of the sample…
Difference-in-difference (DD) estimators are appropriate when the interventions are as good as random, conditional on time and group fixed effects. Although much of the debate in the economic literature regarding DD estimators focuses on the possible endogeneity of the interventions, there is another problem with DD: underestimated standard errors. A paper by Bertrand, Duflo and…
Daniel Kahneman explains the false inference many people make between causation and regression to the mean. A well-known example is the “Sports Illustrated jinx,” the claim that an athlete whose picture appears on the cover of the magazine is doomed to perform poorly the following season. Overconfidence and the pressure of meeting high expectations are…
According to a paper by Zhang, Hauck and Zhao (2013), the answer is ‘no‘. Using a Bayesian approach (i.e., three-level random intercept probit mode) the authors find that different specialties within the same hospital can provide very different quality of care to the patients. The authors summarize their findings as follows: Of the variation in…
According to a paper by Hsee and Zhang, the answer is: not well. Traditional economists and decision theorists assume that people know their preferences and that what they choose reveals what is best for them, given the information they have at the time of choice. In reality, this is not the case. In a series…
The advent of treatment guidelines and pay-for-performance may be a good thing for the “average” patient. However, not all patients respond the same way to given treatments. Thus, just because a treatment benefits the average patient, does not mean that the treatment is beneficial for a given individual patient. This phenomenon is known as treatment…
When conducting a regression-based analysis, how can you determine whether an instrumental variables (IV) method is better than an ordinary least squares (OLS) method? A paper by Basu and Chan (2013) describes one approach based on measuring which of the approaches most successfully reduces the mean square error (MSE) of the coefficient of interest. IV…
In the standard statistics case, when you compare two continuous variables, you can use a t-test to determine equality of means. For instance, you may want to know if LeBron James scores more points on average than Kevin Durant. However, what if you want to instead compare two categorical variables. For instance, assume you are…
What options are available for propensity score matching algorithms? Baser (2006) describes a number of popular options. Stratified Matching. In this method, the range of variation of the propensity score is divided into intervals such that within each interval, treated and control units have, on average, the same propensity score. Differences in outcome measures between…