I wrote a collection of tips and tricks (guide) for R and RStudio (link). This is a work in progress, and I plan to update this in the fiture.
Distributions in cost-effectiveness analysis
In cost-effectiveness analysis, we deal with uncertainty in our parameters by performing sensitivity analyses. In this article, I review how we can generate these distributions for common paramters in a cost-effectiveness analysis. You can view the article at my RPubs page.
Staggered difference-in-differences using R
I was interested in learning how to apply the Callaway & Sant'Anna staggered difference-in-differences framework to my work. After reading several papers and watching the video by Sant'Anna, I wrote a short tutorial on how to apply this framework to a simulated data. The tutorial is located on my RPubs site.
This is a unique method that used the R “did” package, which is based on the paper by Callaway & Sant’Anna.
Mediation analysis using R
It’s not uncommon to see covariates in a regression model that should not be there. For example, measurements that occur after the treatment assignment are included into a regression model as baseline covariates. Rather, one should consider a mediation analysis.
I wrote a tutorial on how to perform mediation analysis using R on my RPubs site (link).
I know that I make this mistake at times. This tutorial helped me to carefully consider which covariates to include in a regression model and which ones to consider for mediation analysis.
R - Loading data from Google drive
Recently, my colleague contacted me to assist another colleague who was having trouble loading data stored on a Google drive account into R. I have never thought about using Google drive as a place to store data and then load it into the R environment. Normally, I store and load data from GitHub, but there are some limitations, particularly when the dataset is very large. Google drive might be an easy workaround to this limitation, so I decided to figure out how to make this work.
I posted this tutorial on my RPubs site.
Presentations with R Markdown - Part 3: Changing font colors
I continue my series on constructing a presentation using R Markdown with a new addition (Part 3) on font colors. We use colors to highlight text to enhance or draw attention to it. In this article, I provide code on how one can do this in R Markdown’s presentation using revealjs.
Part 3 of this series is available on my RPubs site.
MEPS tutorial on interrupted time series analysis in R
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).
MEPS tutorials on linkage files and trend analysis
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).
Exact matching using R - MatchIt package
Recently, I was asked to help create a matching algorithm for a retrospective cohort study. The request was to perform an exact match on a single variable using a 2 to 1 ratio (unexposed to exposed). Normally, I would use a propensity score match (PSM) approach, but the data did not have enough variables for each unique subject. With PSM, I tend to build a logit (or probit) model using variables that would be theoretically associated with the treatment assignment. However, this approach requires enough observable variables to construct these PSM models. For this request, there were a few variables for each subjects; the only variable available were the unique identifier, site, and a continuous variable.
This problem led to a tutorial on how to perform an exact match using the MatchIt package in R, which can be viewed here in my RPubs page.
In this tutorial, you will learn how to perform an exact match with a single variable using a hypothetical dataset with 30 subjects.
Building a book using the bookdown package in R - Part 2: Chapters and References
I create a second tutorial on bookdown that provides a high-level overview on how to add chapters and a reference section. It is located on my RPubs site (link).
I plan on creating more tutorials with the bookdown package, so stay tuned.