Biostatistics and Clinical Research Methodology Unit
• Sample size calculation for case-control study

To do a sample size calculation, you can use the online sample size calculator available at: http://www.math.uiowa.edu/~rlenth/Power/

Worker example

Scenario 1

'S-Syndrome (SS)' is characterized by profound irritability, disorientation and fatigue for those infected individuals. The efficacy of a vaccine (called 'BG vaccine') in preventing adulthood SS remains uncertain, and a study is designed to compare the vaccination coverage rates in a group of MPH students infected with SS and a group of controls with equal sample size. Available information indicates that approximately 30% of the controls are vaccinated. The primary investigator plans to have an 80% chance of detecting an odds ratio significantly different from 1 at the 5% level of significance. If an odds ratio of 2 would be considered an important difference between the two groups, what should the sample size be included in each study group?

Assumptions

Level of significance: 0.05

Statistical power required: 0.8

This can be rearranged as

?

Sample size calculations

Enter p1=0.462, p2=0.3, alpha=0.05.

Adjust sample size until reaching desired power.

Sample size in each group: 152

Total sample size: 304

Scenario 2

If number of cases is limited to 100, untick 'Equal ns', set n1=100, and increase n2 until the power reaches 80%.

The required sample sizes are 100 cases and 293 controls to reach 80% power for OR of 2.

If effect sizes smaller than OR = 2 are of interest, the sample size would be larger. Use the formula shown previously to calculate p1, based on particular values of p2 and OR.

• Bar Chart
• Presents grouped data with rectangular bars with lengths proportional to the values that they represent
• Can be plotted vertically or horizontally
• Very useful for recording discrete data and show comparison
• Histogram
• Represent the distribution of numerical data
• Use for continuous data, where the bins represent ranges of data
• Scatter Plot
• Display values for two variables for a set of data
• Suggest various kinds of correlations between variables
• Ability to show nonlinear relationship between variables

Uncorrelated

High positive
correlated

Low positive
correlated

Negative correlated

Non-linear relationship
• Box Plot

How to understand a Boxplot

• Means and Error Plot
• represent of the mean and variability of data
• represent the overall distribution of the data