Sample size calculation

The calculation of an adequate sample size is crucial to ensuring that the you enrol a sufficient number of participants to stand a reasonable chance of arriving at a meaningful result. This is also an ethical consideration, as it may be difficult to justify subjecting a participant to the risks and burdens of a clinical study if there is little chance of a meaningful conclusion.

As it is almost never feasible to study the whole disease population. Therefore, a set of participants/subjects is selected from the entire population, which is less in number but adequately represents the population from which it is drawn so that true inferences about the population can be made from the results obtained. This set of individuals is known as the sample.

Statistically, the goal of sample size calculation is to minimise the chances of TYPE I ERROR (alpha) and TYPE II ERROR (beta). These errors represent false positives and false negatives, respectively.

There are several factors that needed to be considered when calculating the sample size:

  • Type of statistical test that will be used on the primary outcome
  • The desired POWER (also referred to as ‘1-BETA’; usually 80-90%)
  • The desired false positive rate (also referred to as ‘ALPHA’; usually 5%) [this is what determines the P-value you will consider significant]
  • The estimated effect size [the difference between the two groups] and the outcome rate or mean value in the control and treatment groups.
  • The variability in the primary outcome. Most often defined by the STANDARD DEVIATION.

Estimating the effect size can be challenging as there are often no previously published studies to indicate what sort of effects to expect (if there was you might not be doing your study!). The most common approach is to use evidence from related studies or to use what you think is a reasonable MINIMAL CLINICALLY IMPORTANT DIFFERENCE. You might want to consider a pilot study to inform your understanding of what to expect.

If your primary outcome will be tested using a simple statistical test (eg. t-test, Chi-squared, log-rank test) then there are numerous online calculators that can perform the calculation for you. If you plan to use more complex analytical methods then consultation with a statistician is suggested.

 

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