# epi.sssupb: Sample size for a parallel superiority trial, binary outcome In epiR: Tools for the Analysis of Epidemiological Data

 epi.sssupb R Documentation

## Sample size for a parallel superiority trial, binary outcome

### Description

Sample size for a parallel superiority trial, binary outcome.

### Usage

epi.sssupb(treat, control, delta, n, r = 1, power, nfractional = FALSE, 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 the absolute change in the outcome of interest that represents a clinically meaningful difference. For a superiority trial the value entered for delta must be greater than or equal to zero. 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. nfractional logical, return fractional sample size. alpha scalar, defining the desired alpha level.

### 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.

### Note

Consider a clinical trial comparing two groups, a standard treatment (s) and a new treatment (n). A proportion of subjects in the standard treatment group experience the outcome of interest P_{s} and a proportion of subjects in the new treatment group experience the outcome of interest P_{n}. We specify the absolute value of the maximum acceptable difference between P_{n} and P_{s} as \delta. For a superiority trial the value entered for delta must be greater than or equal to zero.

For a superiority trial the null hypothesis is:

H_{0}: P_{s} - P_{n} = 0

The alternative hypothesis is:

H_{1}: P_{s} - P_{n} != 0

When calculating the power of a study, the argument n refers to the total study size (that is, the number of subjects in the treatment group plus the number in the control group).

For a comparison of the key features of superiority, equivalence and non-inferiority trials, refer to the documentation for epi.ssequb.

### References

Chow S, Shao J, Wang H (2008). Sample Size Calculations in Clinical Research. Chapman & Hall/CRC Biostatistics Series, page 90.

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

Pocock SJ (1983). Clinical Trials: A Practical Approach. Wiley, 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 Chow S, Shao J, Wang H 2008, p. 91):
## Suppose that a pharmaceutical company is interested in conducting a
## clinical trial to compare the efficacy of two antimicrobial agents
## when administered orally once daily in the treatment of patients
## with skin infections. In what follows, we consider the situation
## where the intended trial is for testing superiority of the
## test drug over the active control drug. For this purpose, the following
## assumptions are made. First, sample size calculation will be performed
## for achieving 80% power at the 5% level of significance.

## Assume the true mean cure rates of the treatment agents and the active
## control are 85% and 65%, respectively. Assume the superiority
## margin is 5%.

epi.sssupb(treat = 0.85, control = 0.65, delta = 0.05, n = NA,
r = 1, power = 0.80, nfractional = FALSE, alpha = 0.05)

## A total of 196 subjects need to be enrolled in the trial, 98 in the
## treatment group and 98 in the control group.



epiR documentation built on May 31, 2023, 5:38 p.m.