Interrupted time series analysis (ITSA) with Stata

Interrupted time series analysis (ITSA) is a study design used to study the effects of an intervention across time. An important feature of the ITSA is the time when the intevention occurs. The time before and after the intervention are of interest because we want to visualize if the trends are similar or different. Additionally, we want to visualize the change immediately after the intervention is implemenated. I call this period the index date.

In this article, I’ll review the single-group ITSA and multiple groups ITSA. Then I’ll review how to perform an ITSA in Stata.

You can view the complete tutorial on my RPubs site.

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).

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I plan on creating more tutorials with the bookdown package, so stay tuned.

Building a book using the bookdown package in R - Part 1

I created a tutorial on building an online book using the R bookdown package. The tutorial is available on my RPubs page, which is located here.

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I plan on creating more tutorials with the bookdown package, so stay tuned.

Building an HTML Presentation using R Markdown - Part 2

This is the second part of a series on how to create HTML presentations using R Markdown.

I posted it on my Rpubs site and my GitHub site.

I plan on writing more tutorials on this subject, so stay tuned.

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Tweedie GLM model in R for Cost Data

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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Two-part models in R - Application with cost data

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.

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Building an HTML Presentation Using R Markdown

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.

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Interpreting regression models

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.

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MEPS Tutorial - Part 3: Applying survey weights using R

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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