lower_corr

Pearson's correlation between two binary outcomes takes values between two bounds defined according to the probabilities of the binary outcomes.
This function calculates the lower bound of the correlation based on the probabilities of two binary outcomes.

It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.

Marta Roig

CompAREdesign

Statistical Functions for the Design of Studies with Composite
Endpoints

lower_corr function

<dl><dt>p_e1</dt>
<dd>numeric parameter, probability of the event E1</dd>
<dt>p_e2</dt>
<dd>numeric parameter, probability of the event E2</dd></dl>

Arguments

Lower bound for Pearson's Correlation — lower_corr

<dl>

<dt>p_e1</dt>
<dd>numeric parameter, probability of the event E1</dd>


<dt>p_e2</dt>
<dd>numeric parameter, probability of the event E2</dd>

</dl>

lower_corr: Lower bound for Pearson's Correlation

Description

Usage

Value

Arguments

Details

Examples