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Simpsons Paradox Kidney Stone Study
Patient Health Records & Digital Health
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About
This dataset offers a look into the effectiveness of two distinct kidney stone removal treatments, A (open surgery) and B (percutaneous nephrolithotomy). Originating from a 1986 research paper in The British Medical Journal, it presents a classic example of Simpson's Paradox, where overall trends can contradict those observed within subgroups. It is ideal for logistic regression analysis to assess treatment differences and understand this statistical phenomenon.
Columns
- treatment: Indicates the treatment method used, either Type A (open surgery) or Type B (percutaneous nephrolithotomy).
- stone_size: Describes the size of the kidney stone, categorised as 'small' or 'large'.
- success: Denotes the outcome of the treatment, where 1 indicates success and 0 indicates failure.
Distribution
The data is provided in a CSV file format (
kidney_stone_data.csv) and is approximately 7.73 kB in size. It comprises 700 records, with all columns containing valid and complete data. Both 'treatment' and 'stone_size' columns have two unique categorical values, while 'success' is a binary outcome.Usage
This dataset is particularly useful for:
- Conducting logistic regression analysis to determine the more effective kidney stone removal procedure.
- Illustrating and exploring Simpson's Paradox in a real-world medical context.
- Visualising model outputs to aid medical professionals in treatment decisions.
- Practising and understanding inferential statistics.
Coverage
The dataset’s context is derived from medical research conducted in London in 1986. It encompasses 700 patients and provides insights into treatment outcomes based on different kidney stone sizes, without specific notes on data availability for other demographic groups or years beyond the study period.
License
CC0: Public Domain
Who Can Use It
- Beginners interested in data analysis and statistics.
- Medical professionals and researchers evaluating treatment efficacy.
- Data scientists and analysts for applying logistic regression and exploring statistical paradoxes.
- Students and educators studying inferential statistics, medical data, and historical health studies.
Dataset Name Suggestions
- Kidney Stone Treatment Outcomes 1986
- Simpson's Paradox Kidney Stone Study
- Medical Treatment Efficacy Data
- British Medical Journal Kidney Data
Attributes
Original Data Source: Simpsons Paradox Kidney Stone Study
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