20  Exercises: Hallo Medical Statistics

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TipHallo Medical Statistics

Pre-requicite for these exercises was covered within the session and is therefore not repeated here. The exercises build on this material.

20.1 Learning objectives

The learning objectives for this session are:

  • Udvise forståelse for usikkerheds- og sandsynlighedsbegrebet samt grundliggende begreber indenfor biostatistik
  • Udvise kendskab til de grundliggende overordnede studietyper samt at skelne mellem forklarende, eksplorative og prædiktive studier
  • Redegøre for forskellige typer af tilfældig og ikke tilfældig variation
  • Udvise forståelse for statistiske værktøjers begrænsninger og muligheder
  • Forstå statistiske problemstillinger, der er centrale for medicin med industrial specialisering og forstå, hvordan de biostatistiske værktøjer kan appliceres på disse problemstillinger

20.2 Exercises: “Just send a quick summary”

You have been asked to prepare a quick summary of a dataset of 303 chest pain patients to support a potential clinical trial collaboration. “Nothing complicated—just mean and standard deviation for a few variables.” You open RStudio…

20.2.1 A: Let’s start orienting ourselves in the dataset.

Exercise 1A: Import the Cleveland Heart Disease dataset into R.

  • What is the name of your dataset?
  • How many rows and columns does it have?
  • How many variables does it have?
  • How many participants does it have?
  • Is there any pattern here?

View the dataset sleep_data from the website. Import the data in your RStudio session. How many variables does it have?

Click for the solution. Only click if you are struggling or are out of time.
View(sleep_data)

Exercise 2A: View the dataset.

  • List 3 variables you think are numeric
  • List 2 variables you think are categorical

Why does this distinction matter when calculating a mean?

Click for the solution. Only click if you are struggling or are out of time.
numeric_vars <- c(1,2,3)
categorical_vars <- c(4,5,6)

Exercise 3A:

What happens when you take the mean of a character string? e.g., mean("hi")? Why do you think this happens?

Exercise 4A:

Extract one variable (e.g., Age or Cholesterol) and:

  • Print it to see alt it’s values
  • Calculate its mean

20.2.2 B: Now you start doing the actual request from the email.

Exercise 1B:

Calculate the mean and standard deviation for:

  • Age
  • Chol
  • RestBP
  • MaxHR

Exercise 2B:

Check if there are missing values in the dataset.

  • How many are there and for which variables (tip: view it or use is.nan())?
  • What happens if you try to calculate the mean when missing values are present?

Exercise 3B:

List all the variable types the mean() function can take the mean of. There should be 5. Seek help inside RStudio.

20.2.3 C: You reflect on your day at work during your evening at home…

Exercise 1C:

Consider now what you took the mean and standard deviation of in exercise 3B. How can you interpret this? What can we say from this?

Exercise 2C:

A colleague suggested calculating the mean of Sex.

  • What would that number represent?
  • Is it meaningful? Why or why not?

Exercise 3C:

Why is it important to report both mean and standard deviation, and not just the mean? Think in a clinical context.

20.3 Survey

Feedback survey! 🎉