AE.CI

Computes the attributable effects based confidence interval for the
 average treatment effect on a binary outcome in an experiment where
 \(m\) of \(n\) individuals are randomized to treatment by design.

randomization inference

attributable effects

Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.

Joseph Rigdon

RI2by2

Randomization Inference for Treatment Effects on a Binary
Outcome

AE.CI function

<dl> <dt>data</dt>
<dd>observed 2 by 2 table in matrix form where row 1 is
 the treatment assignment Z=1 and column 1 is the binary outcome Y=1</dd> <dt>level</dt>
<dd>significance level of hypothesis tests, i.e., method yields a 100(1-<code>level</code>)% confidence interval</dd></dl>

Arguments

Joseph Rigdon <a href="/link/jrigdon%40wakehealth.edu?package=RI2by2&version=1.4" data-mini-rdoc="RI2by2::jrigdon@wakehealth.edu">jrigdon@wakehealth.edu</a>

Author

Attributable effects based confidence interval for a treatment effect on a
  binary outcome — AE.CI

<dl>

 <dt>data</dt>
<dd>observed 2 by 2 table in matrix form where row 1 is
 the treatment assignment Z=1 and column 1 is the binary outcome Y=1</dd>

 <dt>level</dt>
<dd>significance level of hypothesis tests, i.e., method yields a 100(1-<code>level</code>)% confidence interval</dd>

</dl>

Joseph Rigdon <a href='mailto:jrigdon@wakehealth.edu'>jrigdon@wakehealth.edu</a>

AE.CI: Attributable effects based confidence interval for a treatment effect on a binary outcome

Description

Usage

Value

Arguments

Author

Details

References

Examples