interval_2p: Sample Size for the confidence interval of the differnce of...
In nutterb/junkyard: Package development playground
Description
Usage
Arguments
Details
Author(s)
References
Description
The $z$-based confidence interval is commonly used in
estimating population mean using sample data. It is based
on the Standard Normal distribution. interval_2p
provides tools for calculating and illustrating the sample
size requirement and other design factors associated with
these intervals.
Usage
1
2
Arguments
E
Margin of error for the difference of
proportions
p1
Proportion observed in group 1.
p2
Proportion observed in group 2.
n
Total sample size for groups 1 and 2.
n1
Sample size for group 1.
n2
Sample size for group 2.
k
proportion of the total samples size n
that consitutes n1. the default assumptions is
that n1 = n2.
alpha
Significance level (or 1-confidence)
Details
This function is currently under development. I needed it
for a study design.
This can be useful for designin equivalence trials.
According to Schumi and Wittes, “Sometimes, the goal is
not to show that the new treatment is better, but that the
new treatment is 'equivalent' to the control...At the end
of the trial, a CI is computed around the difference
between two test statistics (equivalence trials typically
use 90
the two treatments are called 'equivalent.'"
Author(s)
Benjamin Nutter
References
Jennifer Schumi and Janet T Wittes, “ Through the looking
glass: understanding non-inferiority," _Trials_, 2011,
12:106.
nutterb/junkyard documentation built on May 24, 2019, 10:51 a.m.
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Description Usage Arguments Details Author(s) References
Description
The $z$-based confidence interval is commonly used in
estimating population mean using sample data. It is based
on the Standard Normal distribution. interval_2p
provides tools for calculating and illustrating the sample
size requirement and other design factors associated with
these intervals.
Usage
1 2 |
Arguments
E |
Margin of error for the difference of proportions |
p1 |
Proportion observed in group 1. |
p2 |
Proportion observed in group 2. |
n |
Total sample size for groups 1 and 2. |
n1 |
Sample size for group 1. |
n2 |
Sample size for group 2. |
k |
proportion of the total samples size |
alpha |
Significance level (or 1-confidence) |
Details
This function is currently under development. I needed it for a study design.
This can be useful for designin equivalence trials. According to Schumi and Wittes, “Sometimes, the goal is not to show that the new treatment is better, but that the new treatment is 'equivalent' to the control...At the end of the trial, a CI is computed around the difference between two test statistics (equivalence trials typically use 90 the two treatments are called 'equivalent.'"
Author(s)
Benjamin Nutter
References
Jennifer Schumi and Janet T Wittes, “ Through the looking glass: understanding non-inferiority," _Trials_, 2011, 12:106.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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