spline_serumcreat

#'Smoothing splines and KDE residuals pre-calculated from NHANES serum creatinine and
age data by race/ethnicity and gender.

data

httk-pop

Generic models and chemical-specific data for simulation and
statistical analysis of chemical toxicokinetics ("TK") as
described by Pearce et al. (2017) <doi:10.18637/jss.v079.i04>.
Chemical-specific in vitro data have been obtained from relatively
high-throughput experiments. Both physiologically-based ("PBTK")
and empirical (for example, one compartment) "TK" models can be
parameterized with the data provided for thousands of chemicals,
multiple exposure routes, and various species. The models consist
of systems of ordinary differential equations which are solved
using compiled (C-based) code for speed. A Monte Carlo sampler is
included, which allows for simulating human biological variability
(Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
and propagating parameter uncertainty. Calibrated methods are
included for predicting tissue:plasma partition coefficients and
volume of distribution
(Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>).
These functions and data provide a set of tools for
in vitro-in vivo extrapolation ("IVIVE") of high-throughput
screening data (for example, Tox21, ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK")
(Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

John Wambaugh

httk

High-Throughput Toxicokinetics

Sarah Davidson

Robert Pearce

Caroline Ring

Greg Honda

Mark Sfeir

Matt Linakis

Dustin Kapraun

Miyuki Breen

Shannon Bell

Xiaoqing Chang

Todor Antonijevic

Jimena Davis

James Sluka

Nisha Sipes

Barbara Wetmore

Woodrow Setzer

spline_serumcreat function

A data.table with 6 variables:<dl>
<dt><code>gender</code></dt>
<dd>Gender:
 Male or Female</dd> <dt><code>reth</code></dt>
<dd>Race/ethnicity: Mexican American, Other
 Hispanic, Non-Hispanic White, Non-Hispanic Black, Other</dd> <dt><code>sc_spline</code></dt>
<dd>A list of smooth.spline objects, each giving a
 smoothed relationship between log serum creatinine and age in months</dd> <dt><code>sc_kde</code></dt>
<dd>A list of kde
 objects; each is a KDE of the distribution of residuals about the smoothing
 spline.</dd> 
</dl>

Format

Author

Smoothing splines for log serum creatinine vs. age in months, along with KDE
residuals, by race and gender. — spline_serumcreat

A data.table with 6 variables:<dl>
<dt><code>gender</code></dt>
<dd>Gender:
 Male or Female</dd>

 <dt><code>reth</code></dt>
<dd>Race/ethnicity: Mexican American, Other
 Hispanic, Non-Hispanic White, Non-Hispanic Black, Other</dd>

 <dt><code>sc_spline</code></dt>
<dd>A list of smooth.spline objects, each giving a
 smoothed relationship between log serum creatinine and age in months</dd>

 <dt><code>sc_kde</code></dt>
<dd>A list of kde
 objects; each is a KDE of the distribution of residuals about the smoothing
 spline.</dd>

 
</dl>

Smoothing splines for log serum creatinine vs. age in months, along with KDE
residuals, by race and gender.

spline_serumcreat: Smoothing splines for log serum creatinine vs. age in months, along with KDE residuals, by race and gender.

Description

Usage

Arguments

Format

Author

References