epiR (version 2.0.68)

epi.ssequb: Sample size for a parallel equivalence or equality trial, binary outcome

Description

Sample size for a parallel equivalence or equality trial, binary outcome.

Usage

epi.ssequb(treat, control, delta, n, power, r = 1, type = "equivalence", 
   nfractional = FALSE, alpha)

Value

A list containing the following:

n.total

the total number of study subjects required.

n.treat

the required number of study subject in the treatment group.

n.control

the required number of study subject in the control group.

delta

the equivalence limit, as entered by the user.

power

the specified or calculated study power.

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 the absolute change in the outcome of interest that represents a clinically meaningful difference. For an equivalence trial the value entered for delta must be greater than zero.

n

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

power

scalar, the required study power.

r

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

type

a character string indicating the type of analysis to be run. Options are equality and equivalence. If type = equality is selected argument delta is ignored.

nfractional

logical, return fractional sample size.

alpha

scalar, defining the desired alpha level.

References

Bennett J, Dismukes W, Duma R, Medoff G, Sande M, Gallis H, Leonard J, Fields B, Bradshaw M, Haywood H, McGee Z, Cate T, Cobbs C, Warner J, Alling D (1979). A comparison of amphotericin B alone and combined with flucytosine in the treatment of cryptoccal meningitis. New England Journal of Medicine 301, 126 - 131. DOI: 10.1056/NEJM197907193010303.

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.

Wang B, Wang H, Tu X, Feng C (2017). Comparisons of superiority, non-inferiority, and equivalence trials. Shanghai Archives of Psychiatry 29, 385 - 388. DOI: 10.11919/j.issn.1002-0829.217163.

Examples

## 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% and a 
## power of 80% what would be a realistic sample size if the trial were 
## to be repeated?

epi.ssequb(treat = 0.50, control = 0.50, delta = 0.20, n = NA, power = 0.80, 
   r = 1, type = "equivalence", nfractional = FALSE, 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.