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Biostatistics with Python

You're reading from   Biostatistics with Python Apply Python for biostatistics with hands-on biomedical and biotechnology projects

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781837630967
Length 374 pages
Edition 1st Edition
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Author (1):
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Darko Medin Darko Medin
Author Profile Icon Darko Medin
Darko Medin
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Introduction to Biostatistics and Getting Started with Python
2. Chapter 1: Introduction to Biostatistics FREE CHAPTER 3. Chapter 2: Getting Started with Python for Biostatistics 4. Chapter 3: Exercise 1 – Cleaning and Describing Data Using Python 5. Chapter 4: Part 1 Exemplar Project – Load, Clean, and Describe Diabetes Data in Python 6. Part 2:Introduction to Python for Biostatistics – Methodology and Examples
7. Chapter 5: Introduction to Python for Biostatistics 8. Chapter 6: Biostatistical Inference Using Hypothesis Tests and Effect Sizes 9. Chapter 7: Predictive Biostatistics Using Python 10. Chapter 8: Part 2 Exercise – T-Test, ANOVA, and Linear and Logistic Regression 11. Chapter 9: Biostatistical Inference and Predictive Analytics Using Cardiovascular Study Data 12. Part 3:Clinical Study Design, Analysis, and Synthesizing Evidence
13. Chapter 10: Clinical Study Design 14. Chapter 11: Survival Analysis in Biomedical Research 15. Chapter 12: Meta-Analysis – Synthesizing Evidence from Multiple Studies 16. Chapter 13: Survival Predictive Analysis and Meta-Analysis Practice 17. Chapter 14: Part 3 Exemplar Project – Meta-Analysis of Survival Data in Clinical Research 18. Part 4:Biological and Statistical Variables and Frameworks, and a Final Practical Project from the Field of Biology
19. Chapter 15: Understanding Biological Variables 20. Chapter 16: Data Analysis Frameworks and Performance for Life Sciences Research 21. Chapter 17: Part 4 Exercise – Performing Statistics for Biology Studies in Python 22. Index 23. Other Books You May Enjoy

Linear regression for cardiovascular predictive analysis

OK, now, let’s formulate the model based on research question number 1: Is ST depression magnitude associated with the maximum heart rate achieved during a test set? Why is it not easy to formulate this model?

Well, this is because the heart rate is known to be affected by age. Namely, the younger subjects typically have higher heart rates when compared to the older subjects. So, age might affect the value of maximum heart rate and have a confounding effect on the final results. This is especially important when considering the fact that we would like the CAD subjects to be slightly older in our sample.

Now, let’s create the diagram for the potential predictive model:

Figure 9.7 –  The basic stress test model

Figure 9.7 – The basic stress test model

As you can see, the ST depression is the independent variable and the target variable is the stress test maximum heart rate. However, what is also added is a known...

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