epiR (version 0.9-82)

epi.propsize: Sample size, power and minimum detectable difference when comparing proportions

Description

Computes the sample size, power or minimum detectable difference when comparing proportions.

Usage

epi.propsize(treat, control, n, power, r = 1, design = 1,
   sided.test = 2, conf.level = 0.95)

Arguments

treat

the expected value for the treatment group (see below).

control

the expected value for the control group (see below).

n

scalar, defining the total number of subjects in the study (i.e. the number in the treatment and control group).

power

scalar, the required study power.

r

scalar, the number in the treatment group divided by the number in the control group.

design

scalar, the estimated design effect.

sided.test

use a one- or two-sided test? Use a two-sided test if you wish to evaluate whether or not the treatment group is better or worse than the control group. Use a one-sided test to evaluate whether or not the treatment group is better than the control group.

conf.level

scalar, defining the level of confidence in the computed result.

Value

A list containing one or more of the following:

n.crude

the crude estimated total number of subjects required for the specified level of confidence and power.

n.total

the total estimated number of subjects required for the specified level of confidence and power, respecting the requirement for r times as many individuals in the treatment group compared with the control group.

delta

the minimum detectable difference given the specified level of confidence and power.

power

the power of the study given the number of study subjects, the expected effect size and level of confidence.

Details

The methodology in this function follows closely the approach described in Chapter 8 of Woodward (2005).

With this function it is assumed that one of the two proportions is known and we want to test the null hypothesis that the second proportion is equal to the first. Users are referred to the epi.cohortsize function which relates to the two-sample problem where neither proportion is known (or assumed, at least).

Because there is much more uncertainty in the two sample problem where neither proportion is known, epi.cohortsize returns much larger sample size estimates. This function (epi.propsize) should be used in particular situations such as when a politician claims that at least 90% of the population use seatbelts and we want to see if the data supports this claim.

References

Fleiss JL (1981). Statistical Methods for Rates and Proportions. Wiley, New York.

Kelsey JL, Thompson WD, Evans AS (1986). Methods in Observational Epidemiology. Oxford University Press, London, pp. 254 - 284.

Woodward M (2005). Epidemiology Study Design and Data Analysis. Chapman & Hall/CRC, New York, pp. 381 - 426.

Examples

## EXAMPLE 1 (from Woodward 2005 pp. 403 - 404):
## A government initiative has decided to reduce the prevalence of male  
## smoking to, at most, 30%. A sample survey is planned to test, at the 
## 0.05 level, the hypothesis that the percentage of smokers in the male 
## population is 30% against the one-sided alternative that it is greater.
## The survey should be able to find a prevalence of 32%, when it is true,
## with 0.90 power. How many men need to be sampled?

epi.propsize(treat = 0.30, control = 0.32, n = NA, power = 0.90, 
   r = 1, design = 1, sided.test = 1, conf.level = 0.95)
   
## ## A total of 18,315 men should be sampled: 9158 in the treatment group and
## 9158 in the control group.