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

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Description

This function provides detailed sample size estimation information to determine the number of subjects that must be enrolled in a cluster randomized trial to compare two means.

Usage

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n4means(delta, sigma, m, ICC, 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 deviation of the outcome.

m

The anticipated average (or actual) cluster size.

ICC

The anticipated value of the intraclass correlation coefficient, ρ.

AR

The Allocation Ratio: AR=1 implies an equal number of subjects per treatment and control group (maximum efficiency), AR > 1, implies more subjects will be enrolled in the control group (e.g. in the case of costly intervention), AR < 1 implies more subjects in the treatment 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 sample size information, similar to PROC POWER in SAS, but with less functionality and more concise output, and of course, adapted for the design of cluster randomized trial. It is used for sample size estimation in a cluster randomized trial where the outcome is continuous, e.g. blood pressure, or weight. Note that if the results suggest a small number of clusters is required, an iterative procedure will include the T distribution instead of the normal critical value for alpha, iterating until convergence. In some cases, such as small ICC values, the algorithm may fail to converge and may need to be stopped.

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 deviation 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, mrotondi@yorku.ca

References

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

Donner A and Klar N. Design and Analysis of Cluster Randomization Trials in Health Research. Arnold: London, 2000.

See Also

n4props, n4incidence

Examples

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## 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=1, m=25, ICC=0.05, alpha=0.05, power=0.80);