Power analysis for establishing a prevalence

This function can be used to establish how many participants are required to establish a prevalence rate with a given margin of error.

prevalencePower(expectedPrevalence,
                marginOfError = 0.05,
                conf.level = 0.95)

Arguments

expectedPrevalence	The expected prevalence.
marginOfError	The desired precision.
conf.level	The confidence of the confidence interval.

Details

Note that when uncertain as to the expected prevalence, it's better to assume a prevalence closer to 50%. Prevalences closer to 0% or 100% are easier to detect and therefore have more power.

Value

The required number of participants.

See also

convert.percentage.to.se

Examples

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 10% wide:
prevalencePower(.1);
#> [1] 138.2925

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 4% wide:
prevalencePower(.1, .02);
#> [1] 864.3282

### Required participants for detecting a prevalence of 60%
### with a 95% confidence interval of 10% wide:
prevalencePower(.6);
#> [1] 368.78