epiR (version 0.9-82)

epi.noninfb: Estimate the sample size for a parallel non-inferiority trial, binary outcomes

Description

Computes the sample size for a parallel non-inferiority trial with a binary outcome variable.

Usage

epi.noninfb(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

Blackwelder WC (1982). Proving the null hypothesis in clinical trials. Controlled Clinical Trials 3: 345 - 353.

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.

Scott IA (2009). Non-inferiority trials: determining whether alternative treatments are good enough. Medical Journal of Australia 190: 326 - 330.

Zhong B (2009). How to calculate sample size in randomized controlled trial? Journal of Thoracic Disease 1: 51 - 54.

Examples

## EXAMPLE 1:
## Suppose it is of interest to establish non-inferiority of a new treatment 
## as compared to the currently used standard treatment. A difference of less
## than 10% is of no clinical importance. Thus, the non-inferiority margin 
## (delta) is set at 0.10. Assume the true cure rate for the new treatment
## is 0.85 and the control is 0.65. Assuming a one-sided test size of 2.5% and 
## a power of 90% how many subjects should be included in the trial?

epi.noninfb(treat = 0.85, control = 0.65, delta = 0.10, n = NA, r = 1,
   power = 0.80, alpha = 0.025)

## A total of 558 subjects need to be enrolled in the trial, 279 in the 
## treatment group and 279 in the control group.

## EXAMPLE 1 (cont.):
## Suppose only 400 subjects were enrolled in the trial, 200 in the treatment
## group and 200 in the control group. What is the estimated study power?

epi.noninfb(treat = 0.85, control = 0.65, delta = 0.10, n = 400, r = 1,
   power = NA, alpha = 0.025)

## With only 500 subjects the estimated study power is 0.66.