There are all sorts of models out there for performing regressions. This page focuses on 3 that are used for binary outcomes: Getting set up Before you get going, you want to explicitly define your outcome of interest (aka dependent variable), primary exposure (aka independent variable) and covariates that you are adjusting for in your …
Category Archives: Epidemiology and biostatistics
Making a 15x15cm graphical abstract for Hypertension (the AHA journal)
I recently had a paper published in the AHA journal, Hypertension (here: https://www.ahajournals.org/doi/abs/10.1161/HYPERTENSIONAHA.123.22714). The submission required that I include a graphical abstract that was 15×15 cm at 300 dpi and saved in a jpeg format. (That’s 15/2.54*300 = 1772 x 1772 pixels.) I’ve been trying to use EPS files to get around annoying journal image …
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Writing your first scientific conference abstract? Here are some ‘Mad Libs’ documents to get you going.
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 …
Making a table in Stata for regression results (and other output) using frames
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 …
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Part 7: Making a table for your outcome of interest (Table 2?)
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 …
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Part 6: Visualizing your continuous exposure at baseline
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 …
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Part 5: Baseline characteristics in a Table 1 for a prospective observational study
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 …
Descriptive labels of metrics assessing discrimination
Discrimination and calibration of models predicting risk Risk prediction is common in medicine, eg the Framingham Risk Score and the Pooled Cohort Equation/10-year ASCVD risk prediction models. Machine learning models that also predict risk are growing in popularity. Discrimination and calibration are discussed in this excellent JAMA article. In brief, discrimination relates to how a …
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Mediation analysis in Stata using IORW (inverse odds ratio-weighted mediation)
Mediation is a commonly-used tool in epidemiology. Inverse odds ratio-weighted (IORW) mediation was described in 2013 by Eric J. Tchetgen Tchetgen in this publication. It’s a robust mediation technique that can be used in many sorts of analyses, including logistic regression, modified Poisson regression, etc. It is also considered valid if there is an observed …
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ZIP code and county data sets for use in epidemiological research
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 …
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