• 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?

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
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  • 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
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  • Display values for two variables for a set of data


  • Suggest various kinds of correlations between variables
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High positive



Low positive



Negative correlated


Non-linear relationship

  • Box Plot

      How to understand a Boxplot

  • Means and Error Plot

  • represent of the mean and variability of data
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