httkpop_generate: Generate a virtual population for PBTK

In httk: High-Throughput Toxicokinetics

Generate a virtual population for PBTK

Description

Generate a virtual population characterized by demographic, anthropometric, and physiological parameters relevant to PBTK.

Usage

httkpop_generate(
  method,
  nsamp = NULL,
  gendernum = NULL,
  agelim_years = NULL,
  agelim_months = NULL,
  weight_category = c("Underweight", "Normal", "Overweight", "Obese"),
  gfr_category = c("Normal", "Kidney Disease", "Kidney Failure"),
  reths = c("Mexican American", "Other Hispanic", "Non-Hispanic White",
    "Non-Hispanic Black", "Other"),
  gfr_resid_var = TRUE,
  ckd_epi_race_coeff = FALSE
)

Arguments

method	The population-generation method to use. Either "virtual individuals" or "direct resampling." Short names may be used: "d" or "dr" for "direct resampling", and "v" or "vi" for "virtual individuals".
nsamp	The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum	Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
agelim_years	Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If only a single value is provided, both minimum and maximum ages will be set to that value; e.g. agelim_years=3 is equivalent to agelim_years=c(3,3). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
agelim_months	Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If only a single value is provided, both minimum and maximum ages will be set to that value; e.g. agelim_months=36 is equivalent to agelim_months=c(36,36). If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
weight_category	Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
gfr_category	The kidney function categories to include in the population. Default is c('Normal','Kidney Disease', 'Kidney Failure') to include all kidney function levels.
reths	Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
gfr_resid_var	TRUE to add residual variability to GFR predicted from serum creatinine; FALSE to not add residual variability
ckd_epi_race_coeff	TRUE to use the CKD-EPI equation as originally published (with a coefficient changing predicted GFR for individuals identified as "Non-Hispanic Black"); FALSE to set this coefficient to 1.

Details

Demographic and anthropometric (body measures) variables, along with serum creatinine and hematocrit, are generated from survey data from the Centers for Disease Control's National Health and Nutrition Examination Survey (NHANES). Those data are stored in the object nhanes_mec_svy (a survey.design object, see package survey). With method = "d", these variables will be sampled with replacement directly from NHANES data. Each NHANES respondent's likelihood of being sampled is given by their sample weight. With method = "v", these variables will be sampled from distributions fitted to NHANES data. Tissue masses and flows are generated based on demographic, body measures, and serum creatinine values, using regression equations from the literature and/or allometric scaling based on height. Extensive details about how each of these parameters are generated are available in the supplemental material of Ring et al. (2017) (see References for full citation).

Value

A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter. Details of the parameters returned and their units are in the following tables.

Demographic variables

Name	Definition	Units
seqn	NHANES unique identifier (only included if method = "direct resampling")	NA
gender	Sex: "Male" or "Female"	NA
reth	Race/ethnicity: "Non-Hispanic Black", "Non-Hispanic white", "Mexican American", "Other Hispanic", or "Other".	NA
age_years	Age (0-79 years)	years
age_months	Age (0-959 months)	months

Body measures and laboratory measurements

Name	Definition	Units
height	Height	cm
weight	Body weight	kg
serum_creat	Serum creatinine	mg/dL
hematocrit	Hematocrit (percentage by volume of red blood cells in blood)	%

Tissue masses

Name	Definition	Units
Blood_mass	Mass of blood	kg
Brain_mass	Mass of brain	kg
Gonads_mass	Mass of gonads	kg
Heart_mass	Mass of heart	kg
Kidneys_mass	Mass of kidneys	kg
Large_intestine_mass	Mass of large intestine	kg
Liver_mass	Mass of liver	kg
Lung_mass	Mass of lungs	kg
Muscle_mass	Mass of skeletal muscle	kg
Pancreas_mass	Mass of pancreas	kg
Skeleton_mass	Mass of skeleton (including bone, red and yellow marrow, cartilage, periarticular tissue)	kg
Skin_mass	Mass of skin	kg
Small_intestine_mass	Mass of small intestine	kg
Spleen_mass	Mass of spleen	kg
Stomach_mass	Mass of stomach tissue	kg
Other_mass	Mass of GI tract contents (1.4% of body weight) and tissues not otherwise enumerated (3.3% of body weight).	kg
org_mass_sum	Sum of the above tissue masses. A check to ensure this is less than body weight.	kg
Adipose_mass	Mass of adipose tissue. Assigned as weight - org_mass_sum.	kg

Tissue flows

Name	Definition	Units
Adipose_flow	Blood flow to adipose tissue	L/h
Brain_flow	Blood flow to brain tissue	L/h
CO	Cardiac output	L/h
Gonads_flow	Blood flow to gonads tissue	L/h
Heart_flow	Blood flow to heart tissue	L/h
Kidneys_flow	Blood flow to kidneys tissue (not for glomerular filtration!)	L/h
Large_intestine_flow	Blood flow to large intestine tissue	L/h
Liver_flow	Blood flow to liver tissue	L/h
Lung_flow	Blood flow to lung tissue	L/h
Muscle_flow	Blood flow to skeletal muscle tissue	L/h
Pancreas_flow	Blood flow to pancreas tissue	L/h
Skeleton_flow	Blood flow to skeleton	L/h
Skin_flow	Blood flow to skin	L/h
Small_intestine_flow	Blood flow to small intestine	L/h
Spleen_flow	Blood flow to spleen	L/h
Stomach_flow	Blood flow to stomach	L/h
org_flow_check	Sum of blood flows as a fraction of cardiac output (CO). A check to make sure this is less than 1.	Unitless fraction

Adjusted variables

Name	Definition	Units
weight_adj	Adjusted body weight: Sum of all tissue masses.	kg
BSA_adj	Adjusted body surface area, based on height and weight_adj.	cm^2
million.cells.per.gliver	Hepatocellularity	1e6 cells/g liver
gfr_est	Glomerular filtration rate (GFR) estimated using either the CKD-EPI equation (for adults) or a body-surface-area-based equation (for children).	mL/min/1.73 m^2 body surface area
bmi_adj	Body mass index (BMI), adjusted to match weight_adj and height.	kg/m^2
weight_class	Weight category based on bmi_adj: "Underweight" (BMI < 18.5), "Normal" (18.5 < BMI < 24.9), "Overweight" (25.0 < BMI < 29.9), or "Obese" (BMI >= 30)	Unitless category
gfr_class	Kidney function category based on GFR: "Normal" (GFR >=60 mL/min/1.73 m^2), "Kidney Disease" (15 <= GFR <= 60), or "Kidney Failure" (GFR < 15).	Unitless category

Author(s)

Caroline Ring

References

\insertRef

ring2017identifyinghttk

Examples

#Simply generate a virtual population of 100 individuals,
 #using the direct-resampling method
 set.seed(42)
httkpop_generate(method='direct resampling', nsamp=100)

#Generate a population using the virtual-individuals method,
#including 80 females and 20 males,
#including only ages 20-65,
#including only Mexican American and
#Non-Hispanic Black individuals,
#including only non-obese individuals
set.seed(42)
mypop <- httkpop_generate(method = 'virtual individuals',
                          gendernum=list(Female=80,
                          Male=20),
                          agelim_years=c(20,65),
                          reths=c('Mexican American',
                          'Non-Hispanic Black'),
                          weight_category=c('Underweight',
                          'Normal',
                          'Overweight'))
# Including a httkpop.dt argument will overwrite the number of sample and
# the httkpop on/off logical switch:
samps1 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE,
                           httkpop.dt=mypop)
samps2 <- create_mc_samples(chem.name="bisphenola",
                           httkpop.dt=mypop)
samps3 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE)
# Now run calc_mc_oral equiv on the same pop for two different chemcials:
calc_mc_oral_equiv(conc=10,
                   chem.name="bisphenola",
                   httkpop.dt=mypop,
                   return.samples=TRUE)
calc_mc_oral_equiv(conc=2,
                   chem.name="triclosan",
                   httkpop.dt=mypop,
                   return.samples=TRUE)

httk documentation built on Sept. 11, 2024, 9:32 p.m.