epibasix (version 1.5)

n4props: Number of Subjects Required for a Randomized Trial with Binary Outcomes

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 binary outcome.

Usage

n4props(pe, pc, alpha=0.05, power = 0.80, AR=1, two.tailed=TRUE, digits=3)

Arguments

pe

The anticipated proportion of individuals in the experimental group with the outcome.

pc

The anticipated proportion of individuals in the control group with 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

Value

nE

The minimum number of subjects required in the Experimental group.

nC

The minimum number of subjects required in the Control group.

pe

The anticipated proportion of individuals in the experimental group with the outcome.

pc

The anticipated proportion of individuals in the control group with 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

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 response is binary. A simple example may include whether an individual dies from a heart attack. In epidemiological terms, pe and pc can be thought of as the expected prevalence of the outcome in the experimental and control group.

References

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

See Also

n4means

Examples

# NOT RUN {
Suppose a new drug is thought to reduce heart attack mortality from 
0.10 to 0.03. Calculate the required number of subjects that must be enrolled 
in a study to detect this difference with alpha = 0.05 and power = 0.80.
# }
# NOT RUN {
n4props(0.03, 0.10, AR=1, alpha=0.05, power=0.80);
# }