In randomized controlled trials, participants are randomized to different groups where each group receives a unique intervention (or control). This process insures that any differences in the outcomes of interest are due entirely to the interventions under investigation. While RCTs are useful, they are expensive to run, are highly controlled and suffer from their own […]

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

## Berkson’s paradox

Berkson’s paradox happens when given two independent events, if you only consider outcomes where at least one occurs, then they become negatively dependent. More technically, this paradox occurs when there is ascertainment bias in a study design. Let me provide an example. Consider the case where patients can have diabetes or HIV. Assume that patients have a positive probability of […]

Read the rest of this entry »## The problem with p-values

Interesting article in Aeon on why p-values may not be the best way to determine the probability we are observing a real effect in a study. Tests of statistical significance proceed by calculating the probability of making our observations (or the more extreme ones) if there were no real effect. This isn’t an assertion that […]

Read the rest of this entry »## High quality comparative effectiveness research

What are the best practices for conducting comparative effectiveness research in the real-world? One proposed best practice guildelines are the Good Research for Comparative Effectiveness (GRACE) guidelines. However, most studies do not follow these guidelines. A paper by Dreyer, Bryant and Velentgas (2016) assembled 28 observational comparative effectiveness articles published from 2001 to 2010 that compared treatment effectiveness and/or […]

Read the rest of this entry »## The problem with instrumental variables

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 »## 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 […]

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