| n4props {epibasix} | R Documentation |
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 |
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.
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 |
Author(s)
Michael Rotondi, mrotondi@yorku.ca
References
Matthews JNS. Introduction to Randomized Controlled Clinical Trials (2nd Ed.) Chapman & Hall: New York, 2006.
See Also
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.
## End(Not run)
n4props(0.03, 0.10, AR=1, alpha=0.05, power=0.80);