estimate_gfr

<p>A data.table with columns <code>gender</code>, <code>reth</code>, 
<code>age_years</code>, <code>age_months</code>, <code>BSA_adj</code>, <code>serum_creat</code>.</p>

gfrtmp.dt

<p>First predict serum creatinine using smoothing spline, then predict GFR using
CKD-EPI equation.</p>

httk-pop

Functions and data tables for simulation and statistical analysis
of chemical toxicokinetics ("TK") as in 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
(e.g., one compartment) "TK" models can be parameterized for
several hundred chemicals and multiple species. These models are
solved efficiently, often using compiled (C-based) code. A Monte
Carlo sampler is included for simulating biological variability
(Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
and measurement limitations. 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 (e.g., Tox21, ToxCast) to real-world exposures via
reverse dosimetry (also known as "RTK")
(Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

estimate_gfr: Predict GFR.

Description

Usage

Arguments

Value

References