epi.sssupc {epiR} R Documentation

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

### Description

Sample size for a parallel superiority trial, continuous outcome.

### Usage

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

### References

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.

### Examples

```## 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]