I’m a big fan of Stata because of its simplicity and excellent documentation. I have a lot of colleagues that use R and I see why they like it, but I’m not a fan of the R syntax. I’ve played with Python and liked the syntax quite a bit but found simply installing Python and …
Continue reading “Julia for Stata users: Part 1 – getting set up”
Writing the first draft of a scientific conference abstract is challenging. As part of an Early Career Advisory Committee ‘Science Jam’ sponsored by the UVM CVRI, a group of us came up with fill-in-the-blank, Mad Lib-style guide to help guide the completion of the first draft of a scientific conference abstract. There’s one Zip file …
Frames were introduced in Stata 16 and are handy for (a) storing/manipulating multiple datasets simultaneously and (b) building datasets on the fly. I’ve had good luck making a table using frames. This strategy includes (1) making a new frame with as many columns as you need, specifying they are long strings (strL), and printing the …
Continue reading “Making a table in Stata for regression results (and other output) using frames”
The below code outputs this Bland-Altman plot figure, which prints the mean and SD and puts a solid line for mean difference and red dotted lines for mean difference +/- 1.96*SD. (Note: the original BA paper used +/- 2*SD, but it’s reasonable to use +/-1.96*SD given it’s commonality in estimating the bounds of 95% confidence …
Continue reading “Making a Bland-Altman plot with printed mean and SD in Stata”
I recently had to make some scatterplots for Figure 3 of this paper. I decided to clean up the code in case it might be helpful to others. The below code outputs a scatterplot with R-squared and %CV. I grabbed the %CV-from-a-regression code from Mehmet Mehmetoglu’s CV program. (Type –ssc install cv– to grab that …
Continue reading “Making a scatterplot with R squared and percent coefficient of variation in Stata”
As we learned in part 5, Table 1 describes your analytical population at baseline by your exposure. For those using a continuous variable as an exposure, it’s by quantile (e.g., tertile, quartile) of the exposure. I propose a table known as “Table 2” that describes the outcome of interest by the exposure used in Table …
Continue reading “Part 7: Making a table for your outcome of interest (Table 2?)”
Visualization of your continuous exposure in an observational epidemiology research project As we saw in Part 5, it’s important to describe the characteristics of your baseline population by your exposure. This helps readers get a better understanding of internal validity. For folks completing analyses with binary exposures, part 6 isn’t for you. If your analysis …
Continue reading “Part 6: Visualizing your continuous exposure at baseline”
What’s the deal with Table 1? Tables describing the baseline characteristics of your analytical sample are ubiquitous in observational epidemiology manuscripts. They are critical to help the reader understand the study population and potential limitations of your analysis. A table characterizing baseline characteristics is so important that it’s typically the first table that appears in …
The very excellent table1_mc program will automate generation of your Table 1 for nearly all needs (read about it here), except for datasets using pweight. I’ve been toying around with automating Excel table generation using Stata v16+ Frames features. I recently started working on a database that requires pweighting for analyses, and opted to use …
Everyone knows their (5-digit) ZIP and it can be linked to population-level data. ZIP Codes have limitations since they were designed for mail delivery and not for population details. You can easily get county data from these data as well. In epidemiological studies (especially EMR and survey data), you’ll almost certainly have a ZIP code …
Continue reading “ZIP code and county data sets for use in epidemiological research”