biostatistics

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Survival Analysis - Immortal Time Bias with Stata

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I wrote a tutorial on how to handle immortal time bias with survival analysis using Stata. In the tutorial, I used a time-varying predictor for the grouping variable and assigned the period before exposure to the control group. This was inspired by the paper Redelmeier and Singh wrote on “Surival in Academy Award-Winner Actors and Actresses.” There was a lot of debate about the rigor of their analyses, and Sylvestre and colleagues re-analyzed the data with immortal time bias in mind. This tutorial uses data from Sylvestre and colleagues to re-create their results.

The tutorial is on my RPubs page. Data used for the tutorial is located on my GitHub page.

To load the data, you can use the Stata import command

import delimited "https://raw.githubusercontent.com/mbounthavong/Survival-analysis-and-immortal-time-bias/main/Data/data1.csv"
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Sample size estimation using the odds ratio in a case-control study

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I wrote a short tutorial on how to use an odds ratio to estimate the sample size needed for a case-control study.

The tutorial is located on my RPubs page (link)

The R Markdown source code is located on my GitHub site (link)

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Sample size estimation and Power analysis in R

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I wrote a tutorial on how to perform sample size estimations and power analysis using R “pwr” package. These are simple examples that will hopefully lead to more complicated estimations. The tutorial is available on RPubs (link). The R Markdown code is available on my Github site (link).

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Logistic regression in R - Part 2 (Goodness of fit tests)

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In a previous tutorial, I discussed how to perform logistic regression using R. I wrote a follow-up tutorial on how to conduct goodness of fit tests for logistic regression models in R and posted it on RPubs. The R Markdown code is available on my Github site.

I’ve learned how to assess model fit using Pearson correlations, deviance, and modified Hosmer-Lemeshow Goodness Of Fit (GOF) tests. I think these are important tools when assessing the fit of a logistic regression model. However, I wanted to focus on the HL GOF tests for this tutorial because there are a lot of nuances that I learned and wanted to share.

Additionally, I added the usefulness of visualizing whether the model over- or under-predicts the actual observed data using the calibration plot in R.

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Logistic regression in R

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I wrote a tutorial on how to construct logistic regression models in R. This tutorial was published on RPubs (link). I go through the use of the glm() command to perform a crude logistic regression model and a multivariable logistic regression model. The data (diabetes.csv) that I used for the motivating example is located here.

The RMarkdown code I used to create the tutorial is located on my GitHub site (link).

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Visualizing linear regression models using R - Part 1

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I wrote a tutorial on how to visualize linear regression models using R. In the tutorial I used the lm() command and the predict3d package to generate the models and visualize them using R. You can view the RPubs tutorial here. (NOTE: on 30 January 2022, I updated this tutorial and it can be found in my RPubs page here.) I created this tutorial using R Markdown, and the codes are available on my GitHub site (link).