The following simple formula (Daniel, 1999) can be used:
Z 2 P(1− P)
who want to be more confident (say 99%) about their estimates, the value of Z is set at 2.58.
Expected proportion (P): This is the proportion (prevalence) that investigators are going to estimate by the study. Sometimes, investigators feel a bit puzzled and a common
response is that ‘We don’t know this P. That is
why we are going to conduct this study’. We need to understand that the scale of P is from zero to one, and the sample size varies depending on the value of P (Figure 1). Therefore, we have to get an estimate of prevalence (P) in order to calculate the sample size. In many cases, we can get this estimate from previous studies. In this paper, P is in proportion of one, not using a percentage in all formulae. For example, if prevalence is 20%, then P is equal to 0.2.
Precision (d): It is very important for investigators to understand this value well. From the formula, it can be conceived that the sample size varies inversely with the square of the precision (d2).
At the end of a study, we need to present
the prevalence with its 95% confidence interval. For instance, the prevalence in a sample is 40%
n =
d 2