epiR (version 0.9-82)

epi.noninfc: Estimate the sample size for a parallel equivalence trial, continuous outcomes

Description

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

Usage

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

Arguments

treat

the expected mean of the outcome of interest in the treatment group.

control

the expected mean of the outcome of interest in the control group.

sd

the expected population standard deviation of the outcome of interest.

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 (from Chow S, Shao J, Wang H 2008, p. 64):
## A pharmaceutical company is interested in conducting a clinical trial
## to compare two cholesterol lowering agents for treatment of patients with
## congestive hear disease using a parallel design. The primary efficacy 
## parameter is the LDL. In what follows, we will consider the situation
## where the intended trial is for testing non-inferiority of mean responses 
## in LDL. Assume that 80% power is required at a 5% level of significance.

## In this example, we assume a 5% (i.e. delta = 0.05) change of LDL is 
## considered of clinically meaningful difference. Assume the standard 
## of LDL is 0.10 and the LDL concentration in the treatment group is 0.20 
## units and the LDL concentration in the control group is 0.20 units.

epi.noninfc(treat = 0.20, control = 0.20, sd = 0.10, delta = 0.05, n = NA, 
   r = 1, power = 0.80, alpha = 0.05)

## A total of 100 subjects need to be enrolled in the trial, 50 in the 
## treatment group and 50 in the control group.