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epiR (version 0.9-82)

epi.equivb: Estimate the sample size for a parallel equivalence trial, binary outcomes

Description

Computes the sample size for a parallel equivalence trial with a binary outcome variable.

Usage

epi.equivb(treat, control, delta, n, r = 1, power, alpha)

Arguments

treat

the expected proportion of successes in the treatment group.

control

the expected proportion of successes in the control group.

delta

the equivalence limit, expressed as a proportion.

n

scalar, the total number of study subjects in the trial.

r

scalar, the number in the treatment group divided by the number in the control group.

power

scalar, the required study power.

alpha

scalar, defining the desired alpha level.

Value

A list containing one or more of the following:

n.treat

the required number of study subject in the treatment group.

n.control

the required number of study subject in the control group.

n.total

the total number of study subjects required.

References

Chow S, Shao J, Wang H (2008). Sample Size Calculations in Clinical Research. Chapman & Hall/CRC Biostatistics Series, pp. 91.

Ewald B (2013). Making sense of equivalence and non-inferiority trials. Australian Prescriber 36: 170 - 173.

Julious SA (2004). Sample sizes for clinical trials with normal data. Statistics in Medicine 23: 1921 - 1986.

Julious SA (2009). Estimating Samples Sizes in Clinical Trials. CRC, New York.

Machin D, Campbell MJ, Tan SB, Tan SH (2009). Sample Size Tables for Clinical Studies. Wiley Blackwell, New York.

Examples

Run this code
## EXAMPLE 1 (from Machin, Campbell, Tan and Tan 2009 p. 113):
## Bennett, Dismukes, Duma et al. (1979) designed a clinical trial to test 
## whether combination chemotherapy for a shorter period would be at least 
## as good as conventional therapy for patients with cryptococcal meningitis. 
## They recruited 39 patients to each treatment arm and wished to conclude 
## that a difference of less than 20% in response rate between the treatments
## would indicate equivalence. Assuming a one-sided test size of 10%, a 
## power of 80% and an overall response rate of 50%, what would be a 
## realistic sample size if the trial were to be repeated?

epi.equivb(treat = 0.50, control = 0.50, delta = 0.20, n = NA, r = 1, 
   power = 0.80, alpha = 0.10)

## A total of 166 subjects need to be enrolled in the trial, 83 in the 
## treatment group and 83 in the control group.

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