epi.sssupc {epiR} | R Documentation |

Sample size for a parallel superiority trial, continuous outcome.

epi.sssupc(treat, control, sd, delta, n, r = 1, power, nfractional = FALSE, alpha)

`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 the absolute change in the outcome of interest that represents a clinically meaningful difference. For a superiority trial the value entered for |

`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. |

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. |

Consider a clinical trial comparing two groups, a standard treatment (*s*) and a new treatment (*n*). In each group, the mean of the outcome of interest for subjects receiving the standard treatment is *N_{s}* and the mean of the outcome of interest for subjects receiving the new treatment is *N_{n}*. We specify the absolute value of the maximum acceptable difference between *N_{n}* and *N_{s}* as *δ*. 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}: N_{s} - N_{n} = 0*

The alternative hypothesis is:

*H_{1}: N_{s} - N_{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`

.

Chow S, Shao J, Wang H (2008). Sample Size Calculations in Clinical Research. Chapman & Hall/CRC Biostatistics Series, page 61.

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.

## EXAMPLE 1: ## A pharmaceutical company is interested in conducting a clinical trial ## to compare two cholesterol lowering agents for treatment of patients with ## congestive heart disease (CHD) using a parallel design. The primary ## efficacy parameter is the concentration of high density lipoproteins. ## (HDL). We consider the situation where the intended trial is to test ## superiority of the test drug over the active control agent. Sample ## size calculations are to be calculated to achieve 80% power at the ## 5% level of significance. ## In this example, we assume that if treatment results in a 5 unit ## (i.e. delta = 5) increase in HDL it is declared to be superior to the ## active control. Assume the standard deviation of HDL is 10 units and ## the HDL concentration in the treatment group is 20 units and the ## HDL concentration in the control group is 20 units. epi.sssupc(treat = 20, control = 20, sd = 10, delta = 5, n = NA, r = 1, power = 0.80, nfractional = FALSE, 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.

[Package *epiR* version 2.0.38 Index]