prevalencePower: Power analysis for establishing a prevalence

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/prevalencePower.R

Description

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

Usage

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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.

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <[email protected]>

See Also

convert.percentage.to.se

Examples

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

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

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

userfriendlyscience documentation built on Sept. 25, 2018, 9:05 a.m.