When using real-world data, researchers must always deal with a key issue: selection bias. To get around this bias, many health care researchers use an instrumental variable that can predict the explanatory variable of interest (e.g., receipt of a specific treatment) but is not correlated with patient outcomes (e.g., mortality). A commonly used IV is […]

Read the rest of this entry »## Archive for the 'Econometrics' Category

## AA and selection bias

This video that discusses whether alcoholics anonymous actually improves the outcomes of alcoholics who attend the meeting. More broadly, the video the AA treatment effect discussion serves as an example for expounding on some fundamental statistical issues such as selection bias, randomization, intention to treat, marginal effect, instrumental variables, and others.

Read the rest of this entry »## The Intuition behind Bayes Theorem

Bayes Theorem is well-known in law of probability. Mathematically, you could write it as: P(A|B)=P(A and B)/Pr(B) = P(B|A)*P(A)/P(B). An interesting interview in Scientific American with Decision theorist Eliezer Yudkowsky explains Bayes Theorem more intuitively. I might answer that Bayes’s Theorem is a kind of Second Law of Thermodynamics for cognition. If you obtain a […]

Read the rest of this entry »## LOWESS Curves

Often times when doing data analysis, you want to find the relationship between two variables. The first step is typically to plot a scatterplot. To better understand this relationship, however, it is useful to fit a line to the scatterplot. Most commonly, this is done with a simple linear regression (i.e., ordinary least squares (OLS) […]

Read the rest of this entry »## Happy 2016!

Starting off 2016 with some humor: Economists put decimal points in their forecasts to show that they have a sense of humour William Gilmore Simms, on forecasts

Read the rest of this entry »## What are regression trees?

Regression trees are a way to partition your explanatory variables to (potentially) better predict an outcome of interest. Regression trees start with a an outcome (let’s call it y) and a vector of explanatory variables (X). Simple Example For instance, let y be health care spending, X=(X1,X2) where X1 is the patient’s age and X2 is the patient’s […]

Read the rest of this entry »## Synthetic Control Method

A common method for measuring the effect of policy interventions is the difference in difference (DiD) approach. In essence, one examines the change in outcomes among observations subject to the policy intervention and compare them agains observations that were not eligible for the policy intervention. A key assumption for this approach to be valid is […]

Read the rest of this entry »## Falsification Test for Instrumental Variables

Should instrumental variables (IV) be used for real-world evaluation of the comparative effectiveness of different studies? It depends on who you ask.Garabedian et al. (2014) state Although no observational method can completely eliminate confounding, we recommend against treating instrumental variable analysis as a solution to the inherent biases in observational CER studies. On the other hand, Glymour, […]

Read the rest of this entry »## Confirmation Bias

HT: Incidental Economist.

Read the rest of this entry »## Claims-based Measures of Disability

How does one measure disability? This is a difficult question. Many health economists face an even more difficult question: how does one measured disability in claims? A paper by Ben-Shalom and Stapleton attempts to answer this question using six definitions: Chronic Illness and Disability Payment System (CDPS). Developed by Rick Kronick et al. 2000 (one […]

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