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Kidney Stones and Simpson's Paradox

Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.

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  • 8 tasks
  • 997 participants
  • 1,500 XP

Project Description

In this project, you will work with medical data published in 1986 in "The British Medical Journal" where the effectiveness of two types of kidney stone removal treatments (A - open surgery and B - percutaneous nephrolithotomy) were compared.

You will use multiple logistic regression and visualize model output to help the doctors determine if there is a difference between the two treatments.

Before taking this project, we recommend that you have completed Introduction to the Tidyverse and Multiple and Logistic Regression. While not required, it will help to have some knowledge of inferential statistics.

The dataset used in this project is simulated based on the original medical paper published here.

Project Tasks

  • 1A new look at an old research study
  • 2Recreate the Treatment X Success summary table
  • 3Bringing stone size into the picture
  • 4When in doubt, rely on a plot
  • 5Identify and confirm the lurking variable
  • 6Remove the confounding effect
  • 7Visualize model output
  • 8Generate insights
Amy Yang

Senior Data Scientist at Uptake

Amy Yang is a Sr. Data Scientist at Uptake where she conducts industrial analytics and build prediction models to major industries and help them increase productivity, security, safety, and reliability. She began using R for simulation and statistical analysis during her study at the University of Pennsylvania where she received her MS degree in Biostatistics. She also teaches R programming and statistical courses for graduate students.

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    Data VisualizationProbability & StatisticsCase Studies