EventAssignment

<code>EventAssignment</code> Generic background/excess event calculation function

Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

Eric Giunta

Colossus

"Risk Model Regression and Analysis with Complex Non-Linear
Models"

Amir Bahadori

Dan Andresen

Linda Walsh

Benjamin French

Lawrence Dauer

John Boice Jr

Kansas State University 

NASA 

NCRP 

NRC 

EventAssignment function

<dl><dt>x</dt>
<dd>result object from a regression, class poisres</dd>
<dt>df</dt>
<dd>a data.table containing the columns of interest</dd>
<dt>...</dt>
<dd>extended for other necessary parameters</dd></dl>

Arguments

Generic background/excess event calculation function — EventAssignment

<dl>

<dt>x</dt>
<dd>result object from a regression, class poisres</dd>


<dt>df</dt>
<dd>a data.table containing the columns of interest</dd>


<dt>...</dt>
<dd>extended for other necessary parameters</dd>

</dl>

Generic background/excess event calculation function

EventAssignment: Generic background/excess event calculation function

Description

Usage

Arguments