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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

Formulating the scientific questions in life sciences and research

To be able to perform statistical analyses in life science and research, you will first need to learn how to address scientific questions in these areas. Scientific questions are a way to define what it is that we are trying to understand or what goal to achieve. In this chapter, you will learn by example how to formulate scientific questions related to various fields related to biostatistics, such as biomedical research, before any relevant statistical analysis is made. One of the first questions to answer is, “What is the goal of a statistical analysis?” This goal is closely related to different life science aspects, therapies, biological processes, or genetic characteristics, and in this section, those will be covered in more detail.

Once scientific questions are made, they are then used to formulate different scientific hypotheses. The main characteristic of any hypothesis is that it can be tested and there is an alternative (opposite) hypothesis to the main one. So, the baseline scenario assumption can be that there is no statistically significant result, and we can test the alternate scenario: that there is a significant result against the baseline or null scenario. We can call the null hypothesis H0 and the alternate hypothesis Ha.

How to formulate scientific questions related to diabetes

The effect of different lifestyles on the outcomes of type 2 diabetes mellitus has been debated for decades.

Let’s pose a couple of scientific questions about diabetes. We will use the letter Q for scientific questions:

  • Q1. Is body weight related to type 2 diabetes mellitus?
  • Q2. Are there other risk factors for type 2 diabetes mellitus among those studies?
  • Q3. Which of the lifestyle factors is the most important risk factor in type 2 diabetes mellitus?

Now, let’s formulate these questions even better. We will mark formulations using the letter F:

  • F1. Null hypothesis (H0): Body weight is not related to type 2 diabetes mellitus.

    Alternate hypothesis (Ha): Body weight is related to type 2 diabetes mellitus.

  • F2. Null hypothesis (H0): There are no other risk factors for type 2 diabetes mellitus among those studied.

    Alternate hypothesis (Ha): There are other risk factors for type 2 diabetes mellitus among those studied.

  • F3. This question will not have a null hypothesis as it is already assumed there are risk factors in the questions. So, the goal of answering this question is to compare the risk factors and identify the most important one. This would be an observational scientific question.

So, why do we usually formulate the null hypothesis as a negation of what’s being tested? Well, we want to know the following: Can I show evidence that contradicts that baseline negative assumption? If I can, then I can reject the null hypothesis. If there isn’t enough evidence to negate the null hypothesis, I can say that I cannot reject the null hypothesis (avoid the mistake of saying that no evidence is evidence of a null hypothesis).

How to formulate scientific questions related to cardiovascular disease

Is ST (the last wave on the electrocardiogram of the heartbeat) elevation closely related to heart disease? With this, we move to the following questions:

  • Q4. Do cigarettes increase the risk of cardiovascular diseases?
  • Q5. Is an ECG closely related to cardiovascular disease?
  • Q6. Are there any other risk factors for cardiovascular disease among the studied parameters?

    Let us make a more structured formulation as follows:

  • F4. Null hypothesis (H0): Cigarettes do not increase the risk of cardiovascular diseases.
  • Alternate hypothesis (Ha): Cigarettes increase the risk of cardiovascular diseases.
  • F5. Null hypothesis (H0): ECG is not closely related to cardiovascular disease.
  • Alternate hypothesis (Ha): ECG is not closely related to cardiovascular disease.
  • F6. Practice yourself!

How to formulate scientific questions in biology

Here are a few examples for formulating questions in biology:

  • Q7. Learn to explore which genes are highly suppressed in lung cancer.
  • Q8. How similar are the genomes of mice and humans?
  • Q9. What are the differences in plants and minerals collected from localities A and B (Ca, Mg, K)?
  • Q10. Does water temperature affect plankton?

Practice formulating these questions as hypotheses or concrete study questions!

You may find the answers at the end of Chapter 1.

You have been reading a chapter from
Biostatistics with Python
Published in: Nov 2024
Publisher: Packt
ISBN-13: 9781837630967
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