epiR (version 0.9-82)

epi.meansize: Sample size, power and minimum detectable difference when comparing means

Description

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

Usage

epi.meansize(treat, control, n, sigma, 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).

sigma

the expected standard deviation of the variable of interest for both treatment and control groups.

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).

References

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 p. 399):
## Supposed we wish to test, at the 5% level of significance, the hypothesis
## that cholesterol means in a population are equal in two study years against 
## the one-sided alternative that the mean is higher in the second of the 
## two years. Suppose that equal sized samples will be taken in each year, 
## but that these will not necessarily be from the same individuals (i.e. the 
## two samples are drawn independently). Our test is to have a power of 0.95 
## at detecting a difference of 0.5 mmol/L. The standard deviation of serum 
## cholesterol in humans is assumed to be 1.4 mmol/L. 

epi.meansize(treat = 5, control = 4.5, n = NA, sigma = 1.4, power = 0.95, 
   r = 1, design = 1, sided.test = 1, conf.level = 0.95)

## To satisfy the study requirements 340 individuals need to be tested: 170 in
## the first year and 170 in the second year.


## EXAMPLE 2 (from Woodward 2005 pp. 399 - 400):
## Women taking oral contraceptives sometimes experience anaemia due to 
## impaired iron absorption. A study is planned to compare the use of iron
## tablets against a course of placebos. Oral contraceptive users are 
## randomly allocated to one of the two treatment groups and mean serum
## iron concentration compared after 6 months. Data from previous studies
## indicates that the standard deviation of the increase in iron
## concentration will be around 4 micrograms% over a 6-month period.
## The average increase in serum iron concentration without supplements is
## also thought to be 4 micrograms%. The investigators wish to be 90% sure
## of detecting when the supplement doubles the serum iron concentration using
## a two-sided 5% significance test. It is decided to allocate 4 times as many
## women to the treatment group so as to obtain a better idea of its effect.
## How many women should be enrolled in this study?

epi.meansize(treat = 8, control = 4, n = NA, sigma = 4, power = 0.90, 
   r = 4, design = 1, sided.test = 2, conf.level = 0.95)
   
## The estimated sample size is 66. We round this up to the nearest multiple
## of 5, to 70. We allocate 70/5 = 14 women to the placebo group and four
## times as many (56) to the iron treatment group.