n4means {epibasix}

Number of Subjects Required for a Randomized Trial with a Continuous Outcome

Description

This function provides detailed sample size estimation information to determine the number of subjects that must be enrolled in a randomized trial with a continuous outcome.

Usage

n4means(delta, sigma, alpha=0.05, power=0.8, AR=1, two.tailed=TRUE, digits=3)

Arguments

delta

The minimum detectable difference between population means.

sigma

The standard error of the outcome.

AR

The Allocation Ratio: One implies an equal number of subjects per treatment and control group (maximum efficiency), > 1, implies more subjects will be enrolled in the control group (e.g. in the case of costly intervention), < 1 implies more in the tretment group (rarely used).

alpha

The desired Type I Error Rate

power

The desired level of power, recall power = 1 - Type II Error.

two.tailed

Logical, If TRUE calculations are based on a two-tailed Type I error, if FALSE, a one-sided calculation is performed.

digits

Number of Digits to round calculations

Details

This function provides detailed information, similar to PROC POWER in SAS, but with less functionality and more concise output. It is used for sample size estimation in a randomized trial where the outcome is continuous, such as blood pressure, or weight.

Value

nE

The minimum number of subjects required in the Experimental group.

nC

The minimum number of subjects required in the Control group.

delta

The minimum detectable difference between population means.

sigma

The standard error of the outcome.

alpha

The desired Type I Error Rate

power

The desired level of power, recall power = 1 - Type II Error.

AR

The Allocation Ratio

Author(s)

Michael Rotondi, [e-mail]

References

Matthews JNS. Introduction to Randomized Controlled Clinical Trials (2nd Ed.) Chapman & Hall: New York, 2006.

See Also

n4props

Examples

## Not run: Suppose we wish to test whether a blood pressure medication reduces diastolic blood
pressure by 10 mm Hg, at standard significance and power, assume the standard deviation is 10 mm Hg.
## End(Not run)
n4means(delta=10, sigma=10, alpha=0.05, power=0.80);