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Course Information
Meet the Teaching Team -
Course Dataset 1
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Course Dataset 2
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MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSSA1.1 What is Statistics?
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A1.2.1a Introduction to Stata
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A1.2.2b: Introduction to R
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A1.2.2c: Introduction to SPSS
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A1.3: Descriptive Statistics
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A1.4: Estimates and Confidence Intervals
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A1.5: Hypothesis Testing
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A1.6: Transforming Variables
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End of Module A11 Quiz
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MODULE A2: POWER & SAMPLE SIZE CALCULATIONSA2.1 Key Concepts
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A2.2 Power calculations for a difference in means
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A2.3 Power Calculations for a difference in proportions
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A2.4 Sample Size Calculation for RCTs
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A2.5 Sample size calculations for cross-sectional studies (or surveys)
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A2.6 Sample size calculations for case-control studies
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End of Module A21 Quiz
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MODULE B1: LINEAR REGRESSIONB1.1 Correlation and Scatterplots
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B1.2 Differences Between Means (ANOVA 1)
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B1.3 Univariable Linear Regression
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B1.4 Multivariable Linear Regression
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B1.5 Model Selection and F-Tests
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B1.6 Regression Diagnostics
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End of Module B11 Quiz
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MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURESB2.1 ANOVA Revisited – Post-Hoc Testing
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B2.2 Correcting For Multiple Comparisons
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B2.3 Two-way ANOVA
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B2.4 Repeated Measures and the Paired T-Test
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B2.5 Repeated Measures ANOVA
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End of Module B21 Quiz
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MODULE B3: NON-PARAMETRIC MEASURESB3.1 The Parametric Assumptions
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B3.2 Mann-Whitney U Test
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B3.3 Kruskal-Wallis Test
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B3.4 Wilcoxon Signed Rank Test
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B3.5 Friedman Test
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B3.6 Spearman’s Rank Order Correlation
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End of Module B31 Quiz
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MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSIONC1.1 Introduction to Prevalence, Risk, Odds and Rates
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C1.2 The Chi-Square Test and the Test For Trend
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C1.3 Univariable Logistic Regression
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C1.4 Multivariable Logistic Regression
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End of Module C11 Quiz
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MODULE C2: SURVIVAL DATAC2.1 Introduction to Survival Data
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C2.2 Kaplan-Meier Survival Function & the Log Rank Test
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C2.3 Cox Proportional Hazards Regression
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C2.4 Poisson Regression
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End of Module C21 Quiz
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A Note about the Fossa Certificate
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
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