QuantileAnalysis

A function for quantile analysis that provides a p-value and a point estimation.

Methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level.
User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.

Yang Dong

Ricrt

Randomization Inference of Clustered Randomized Trials

Bingkai Wang

Dylan Small

QuantileAnalysis function

<dl><dt>S</dt>
<dd>A numeric column vector with individuals' stratum number</dd>
<dt>C</dt>
<dd>A numeric column vector with individuals' cluster number</dd>
<dt>Z</dt>
<dd>A numeric column vector with individuals' treatment assignment
(binary)</dd>
<dt>R</dt>
<dd>A numeric column vector with individuals' outcome</dd>
<dt>quantile</dt>
<dd>A numeric value for intended quantile in the input data for
running the quantile test, the default of this value is 0.5.</dd>
<dt>delta_hyp</dt>
<dd>A numeric value that is the hypothesized treatment effect
given the quantile. The default of this value is 0.</dd></dl>

Arguments

Quantile Effect Analysis — QuantileAnalysis

<dl>

<dt>S</dt>
<dd>A numeric column vector with individuals' stratum number</dd>


<dt>C</dt>
<dd>A numeric column vector with individuals' cluster number</dd>


<dt>Z</dt>
<dd>A numeric column vector with individuals' treatment assignment
(binary)</dd>


<dt>R</dt>
<dd>A numeric column vector with individuals' outcome</dd>


<dt>quantile</dt>
<dd>A numeric value for intended quantile in the input data for
running the quantile test, the default of this value is 0.5.</dd>


<dt>delta_hyp</dt>
<dd>A numeric value that is the hypothesized treatment effect
given the quantile. The default of this value is 0.</dd>

</dl>

QuantileAnalysis: Quantile Effect Analysis

Description

Usage

Value

Arguments

Examples