joint_test

Hypothesis testing about joint effect of exposures on a multinomial outcome

G-computation for a set of time-fixed exposures with
quantile-based basis functions, possibly under linearity and
homogeneity assumptions. This approach estimates a regression line
corresponding to the expected change in the outcome (on the link
basis) given a simultaneous increase in the quantile-based category
for all exposures. Works with continuous, binary, and right-censored
time-to-event outcomes. Reference: Alexander P. Keil, Jessie P.
Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
Alexandra J. White (2019) A quantile-based g-computation approach to
addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.

Alexander Keil

qgcomp

Quantile G-Computation

joint_test function

<dl><dt>x</dt>
<dd>Result from fit.</dd>
<dt>...</dt>
<dd>Unused</dd></dl>

Arguments

Hypothesis testing about joint effect of exposures on a multinomial outcome — joint_test

<dl>

<dt>x</dt>
<dd>Result from fit.</dd>


<dt>...</dt>
<dd>Unused</dd>

</dl>

joint_test: Hypothesis testing about joint effect of exposures on a multinomial outcome

Description

Usage

Arguments