ColossusLogitSurv

<code>ColossusLogitSurv</code> assigns and interprets columns for trials and events in logistic model with no grouping.

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 

ColossusLogitSurv function

<dl><dt>...</dt>
<dd>entries for a Logistic object, trials and events. trials not provided assumes one trial per row.</dd></dl>

Arguments

Interprets basic logistic survival formula RHS with no grouping — ColossusLogitSurv

<dl>

<dt>...</dt>
<dd>entries for a Logistic object, trials and events. trials not provided assumes one trial per row.</dd>

</dl>

Interprets basic logistic survival formula RHS with no grouping

ColossusLogitSurv: Interprets basic logistic survival formula RHS with no grouping

Description

Usage

Value

Arguments

See Also