MEPS

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Propensity score matching in R

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I wrote an introductory tutorial on how to perform propensity score matching using R, which has been posted on my RPubs site (link).

Propensity score matching is a statistical approach to balancing the observed covariates between groups. In observational studies, this method has the potential to mitigate potential confounding and allow us to make causal interpretations. However, there are a lot of approaches and nuances. This intorductory tutorial presents the basics of propensity score methods and how we can use these in our conventional analyses.

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Stata - marginsplot & mplotoffset commands for plotting average marginal effects

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In Stata, users have a lot of flexibility with creating plots, particularly after the margins command has been executed. Once a regression command has been run, users can estimate the average marginal effect of a factor with respect to another variable using the margins command in Stata. Once the average marginal effect has been estimated, users can plot this using the marginsplot or mplotoffset commands. These are power tools that allow us to visualize the average marginal effects, particularly when we have interaction terms.

I posted a tutorail on my RPubs site that revieweed some basic features of the marginsplot and mplotoffset commands and provide some practical examples of customization.

MEPS Tutorial - Some of my helpful notes

There are a lot of lessons that I’ve learned from using the Medical Expenditure Panel Survey (MEPS) data from the Agency for Healthcare Research and Quality (AHRQ). Some of these I learned after I made some mistakes and some I learned from other people. Overall, it’s a short but evolving note of the things that I’ve learned about MEPS and its nuances. I plan on updating this in the future as I expect to learn more new things. But for those who are interested in learning what I’ve learned, you can read my notes on my RPubs page, which is here.

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MEPS tutorial on interrupted time series analysis in R

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I wrote a short tutorial on how to perform an interrupted time series analysis in R. I had a challenging time working on this because I wasn’t familiar with all the nuances of the ITSA. More importantly, I wasn’t able to leverage my Stata skills to do this in R. I’m used to the Stata margins command, which is great for creating constrasts. R has its own version of the margins command, but it lacks some of Stata’s features such as the pwcompare, which I use a lot in Stata. However, I found a workaround with linear splines, and I have uploaded this to my RPubs site (link). I hope you find this useful. I also saved my R Markdown code on my GitHub site (link).

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MEPS tutorials on linkage files and trend analysis

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I create two MEPS tutorials recently. One is on the use of condition-event linkage files to capture the disease-specific costs. I used migraine as a motivating example. In this tutorial, I go through the steps to identify migraine-related costs assocaited with office-based visits and inpatient night stays. In the second tutorial, I review how to perform simple trend analysis with linear regressio models. I pooled MEPS data from 2016 to 2021 and apply the approriate primary sampling units and strata from the pooled file.

The first tutorial is located on my RPubs page (MEPS Tutorial 4 - Using condition-event link (CLNK) file: A case study with migraine). The R Markdown code to create the tutorial is located in my GitHub repository (link).

The second tutorial is also located on my Rpubs page (MEPS Tutorial 5 - Simple Trend Analysis with Linear Models). The R Markdown code to create the tutorial is located in my GitHub repository (link).

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Tweedie GLM model in R for Cost Data

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I wrote a tutorial on using a Tweedie distribution for a GLM gamma model for cost data in R. Unlike Stata, R is very particular with zeroes when constructing GLM models. Hence, I opted to use the Tweedie distribution to mix and match the link function with the Gamma distribution. I settled on the identity link because it doesn’t involve retransformation and is each to interpret.

My tutorial is available on my RPubs site and GitHub site.

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MEPS Tutorial - Part 3: Applying survey weights using R

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In this tutorial, we will review how survey weights from the Medical Expenditure Panel Survey (MEPS) are applied using R.

The tutorial is available on my GitHub site and RPubs.

The R Markdown code I used to generate this tutorial is available on my GitHub site.

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MEPS Tutorial - Part 1: Loading Data into R

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For the last couple of years, I have used Stata whenever I worked with MEPS data. Stata is a great statistical program that allows me to script and analyze data from complex survey designs similar to MEPS. However, R is another powerful statistical program that researchers have been using to evaluate and analyze MEPS data. R is free/open source and has a large community that constantly builds packages to improve its utility. Because of its advantages, I wanted to start writing tutorials on how to use R to analyze MEPS data.

This first tutorial provides instructions on how to load MEPS data into R, which is a critical step for data analysis.

You can find the tutorial on my RPubs page (link); I also posted this on my GitHub page (link).

For those of you who are interested in how I developed this tutorial, the R Markdown code is located on my GitHub page (link).

In the coming months, I’ll continue to write more tutorials using R with MEPS data, so stay tuned.

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Stata tutorial: Adding the 95% Confidence Interval to a Two-way Line Plot

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I created a tutorial on how to add the 95% CI to a two-way line plot in Stata. I use the “connected” command to generate a line plot in Stata, and then I added the 95% CI to each value. Surprisingly, Stata does not have a native feature to allow users to generate these 95% CI on a two-way line plot.

I used the AHRQ Medical Expenditure Panel Survey (MEPS) database for the motivating example. In this tutorial, we plotted the average total healthcare expenditure from 2008 to 2019.

I build this tutorial on Stata, but I used R Markdown to write the tutorial. The R Markdown code is located in my GitHub site (Stata - Line plot with 95% CI tutorial).

You can find the tutorial on my Github site and RPubs page.

I used Stata SE 17 to build this.