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FoSSA: Fundamentals of Statistical Software & Analysis

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  1. Course Information

    Meet the Teaching Team
  2. Course Dataset 1
  3. Course Dataset 2
  4. MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSS
    A1.1 What is Statistics?
  5. A1.2.1a Introduction to Stata
  6. A1.2.2b: Introduction to R
  7. A1.2.2c: Introduction to SPSS
  8. A1.3: Descriptive Statistics
  9. A1.4: Estimates and Confidence Intervals
  10. A1.5: Hypothesis Testing
  11. A1.6: Transforming Variables
  12. End of Module A1
    1 Quiz
  13. MODULE A2: POWER & SAMPLE SIZE CALCULATIONS
    A2.1 Key Concepts
  14. A2.2 Power calculations for a difference in means
  15. A2.3 Power Calculations for a difference in proportions
  16. A2.4 Sample Size Calculation for RCTs
  17. A2.5 Sample size calculations for cross-sectional studies (or surveys)
  18. A2.6 Sample size calculations for case-control studies
  19. End of Module A2
    1 Quiz
  20. MODULE B1: LINEAR REGRESSION
    B1.1 Correlation and Scatterplots
  21. B1.2 Differences Between Means (ANOVA 1)
  22. B1.3 Univariable Linear Regression
  23. B1.4 Multivariable Linear Regression
  24. B1.5 Model Selection and F-Tests
  25. B1.6 Regression Diagnostics
  26. End of Module B1
    1 Quiz
  27. MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURES
    B2.1 ANOVA Revisited - Post-Hoc Testing
  28. B2.2 Correcting For Multiple Comparisons
  29. B2.3 Two-way ANOVA
  30. B2.4 Repeated Measures and the Paired T-Test
  31. B2.5 Repeated Measures ANOVA
  32. End of Module B2
    1 Quiz
  33. MODULE B3: NON-PARAMETRIC MEASURES
    B3.1 The Parametric Assumptions
  34. B3.2 Mann-Whitney U Test
  35. B3.3 Kruskal-Wallis Test
  36. B3.4 Wilcoxon Signed Rank Test
  37. B3.5 Friedman Test
  38. B3.6 Spearman's Rank Order Correlation
  39. End of Module B3
    1 Quiz
  40. MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSION
    C1.1 Introduction to Prevalence, Risk, Odds and Rates
  41. C1.2 The Chi-Square Test and the Test For Trend
  42. C1.3 Univariable Logistic Regression
  43. C1.4 Multivariable Logistic Regression
  44. End of Module C1
    1 Quiz
  45. MODULE C2: SURVIVAL DATA
    C2.1 Introduction to Survival Data
  46. C2.2 Kaplan-Meier Survival Function & the Log Rank Test
  47. C2.3 Cox Proportional Hazards Regression
  48. C2.4 Poisson Regression
  49. End of Module C2
    1 Quiz

Learning Outcomes

By the end of this section, students will be able to:

  • Explain the importance of the parametric assumptions and determine if they have been met
  • Explain the basic principles of rank based non-parametric statistical tests
  • Describe the use of a range of common non-parametric tests
  • Conduct and interpret common non-parametric tests

You can download a copy of the slides here: B3.4 Wilcoxon Signed Rank Test

B3.4 PRACTICAL: R

We wish to use the Wilcoxon Signed Rank Test to determine if there is a significant difference in body condition score across all mice in the study between the start (BCS_baseline) and end (BCS_end) of the trial.

We use the wilcox.test command to perform this test in R. We specify the two variables, and must use paired=TRUE since the measurements are coming from the same subject:

> wilcox.test(data$BCS_baseline, data$BCS_end, paired = TRUE, exact = FALSE)

The RStudio output looks like this:

There is no significant difference (p>0.05) in body condition score of the mice between the start and the end of the study.

Question B3.4: Is there a significant difference between weight at the beginning and weight at the end?

Answer

We use the following R code:

> wilcox.test(data$Weight_end, data$Weight_baseline, paired = TRUE, exact = FALSE)

The RStudio output looks like this:

From this, we can conclude that there is no significant difference (p>0.05) in the weight of the mice between the start and the end of the study.

B3.4 PRACTICAL: Stata

Use the Wilcoxon Signed Rank Test to determine if there is a significant difference in body condition score across all mice in the study between the start (BCS_baseline) and end (BCS_end) of the trial.

We use the ‘signrank’ command to perform this test in Stata:

Here we can conclude that there is no significant difference in body condition score of the mice between baseline and end of the trial (p=1.00).

Question B3.4: Is there a significant difference between weight at the beginning and weight at the end?

Answer

And based on this we can also say that there is no significant difference in weight of the mice between baseline and end of the trial (p=0.93).

B3.4 PRACTICAL: SPSS

Use the Wilcoxon Signed Rank Test to determine if there is a significant difference in body condition score across all mice in the study between the start (BCS_baseline) and end (BCS_end) of the trial.

Select

Analyze >> Nonparametric Tests  >> Legacy Dialogs >> 2 Related Samples

SPSS assumes that each row is a separate participant or case, so for all repeated measures tests it requires each measure to be a separate variable.

Move the two variables you are interested in into the spaces for Variable 1 and Variable 2 in Pair 1.

Make sure Wilcoxon is selected at the bottom of the box before you press ‘OK’ to run the test.

You will notice that a second blank ‘pair’ is automatically created when you have completed the first pair. Also, your variables do not disappear from the box on the left hand side as they do in the majority of tests. This is because you can create multiple pairs to test in one go, and you can compare one variable to any number of variables.

Run the analysis again, but add in a comparison of weight at the beginning and weight at the end as well. 

Answer

Here we can conclude that there is no significant difference in body condition score of the mice between baseline and end of the trial.

And based on this we can also say that there is no significant difference in weight of the mice between baseline and end of the trial.

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