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]