“Internal Validity”: A blog about stuff
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.
I created a tutorial on how to use two-part models in R for cost data. I used the healthcare expenditures from the Medical Expenditure Panel Survey in 2017 as a motivating example. Normally, I use Stata when I construct two-part models. But I wanted to learn how I could do this in R. Fortunately, R has a package called twopartm that was developed by Duan and colleagues. You can find their document for the twopartm package here.
The tutorial I created is located on my GitHub page and RPubs page.
I learned how to construct an HTML presentation using R Markdown. In order to help myself remember how to do this, I began writing a tutorial. I posted the first part of the tutorial on my RPubs page.
I plan to write more tutorials on this subject, so stay tuned.
I wrote a short explanation on how to interpret regression models.
I have posted this on RPubs, and the code is saved on my GitHub site.
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.
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.
My colleagues, Drs. Sankeet Shah and Koen Degeling, hosted a conversation with Dr. Julia Slejko from the University of Maryland and Dr. Aryana Sepassi from the University of California, Irvine about their experiences and strategies for networking at ISPOR conferences. Dr. Slejko is an Associate Professor of Practice, Science, and Health Outcomes and has a wealth of experiences attending ISPOR conferences. Dr. Aryana Sepassi is an Assistant Professor of Outcomes Research and has just attend her first ISPOR conference in May 2022. Both guest share their unique perspectives on networking at these large conferences and provide suggestions on how to maximize your experience.
You can listen to their conversation on Soundcloud.
I discovered an interesting package that allowed me to insert icons into my R Markdown documents. I learned how to use some of the basic commands and wrote a short tutorial on how to do this. I posted the tutorial on my GitHub page. I also posted the R Markdown code on my GitHub site.
I also encourage you to check out the Font Awesome GitHub page to learn more about the different icons that are available.
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.
