Back to Course

FoSSA: Fundamentals of Statistical Software & Analysis

0% Complete
0/0 Steps
  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:

  • Describe the characteristics of survival data
  • Set up your software programme to analyse survival data

 

Video C2.1- Introduction to Survival Data (9 minutes)

 

 

C2.1a PRACTICAL: Stata     

Setting up survival data

The command ‘stset’ needs to be specified first. This command tells Stata you are analysing survival-time data, and you therefore need to specify the time variable and the variable that defines the event (i.e. failure).

Following the command ‘stset’ comes the name of the “time to event” variable, which is your outcome variable. This is followed by the option which specifies an event [failure()], with 0=censorship. If you do not specify this option, Stata assumes that there is no censoring.

Since we want to look at the event of “death”, and we have followed participants for several years, the following command is used:

stset fu_years, failure(death) id(whl1_id)

Notice the output from Stata. The ‘stset’ command adds four variables to the dataset: “_t” is the time to event variable; “_d” indicates if the participant was censored (0) or had an event (1); “_t0” denotes the beginning of the time variable, with time 0 as default; and “_st” indicates which rows are being used in the analysis, with all coded 1 for default.

Question C2.1a:  How many events occurred during the follow up period? 

Answer

 1,526 deaths

 

C2.1 PRACTICAL: SPSS

There is no practical material here for SPSS

 

C2.1 PRACTICAL: R

The command “Surv” is used to create a survival object, which sets up a time to event data structure. When we have only one time period, like in this study, we specify in the form Surv(follow up time, event indicator). 

Since we want to look at the event of “death”, and we have followed participants for several years, the following command is used:

Surv(white.data$fu_time, white.data$death)

The output from R is a matrix with two columns – the first is the survival time, and the second an indicator for death or not.

Question C2.1a:  How many events occurred during the follow up period? 

Answer

 1,526 deaths

 

👋 Before you go, please rate your satisfaction with this lesson

Ratings are completely anonymous

Average rating 0 / 5. Vote count: 0

No votes so far! Be the first to rate this post.

Please share any positive or negative feedback you may have.

Feedback is completely anonymous

Subscribe
Notify of
guest

1 Comment
Newest
Oldest Most Voted
Inline Feedbacks
View all comments
Sayed Jalal

No presentation. Please add downloadable presentation

1
0
Questions or comments?x