Calculate risk sensitivity to a single variable

Compute risk by varying one variable while holding others fixed.

Usage

calc_sensitivity(
  GT,
  vary = c("age", "weight", "css_params", "fit_params", "C_ext"),
  overwrite = FALSE,
  rate_extra_cols = NULL,
  expos_mean = NULL,
  expos_sd = NULL,
  max_mult = 1.5
)

Arguments

GT: GeoTox object.
vary: Variable to vary. One of "age", "weight", "css_params", "fit_params", or "C_ext".
overwrite: Logical indicating whether to overwrite existing sensitivity analysis results in the GeoTox database.
rate_extra_cols: Additional columns to match from the 'exposure_rate_params' table (default NULL).
expos_mean: Column name of exposure concentration mean in the 'exposure' table (default "mean").
expos_sd: Column name of exposure concentration standard deviation in the 'exposure' table (default "sd").
max_mult: Upper bound multiplier for max response (default 1.5).

Value

The updated GeoTox object, invisibly.

Details

The sensitivity analysis makes use of the C$_{ss}$ values stored in the 'fixed_css' table of the GeoTox database. These values are determined using the pre-simulated C$_{ss}$ values supplied to set_simulated_css() and can be set using set_fixed_css() prior to running this function. This step is automatically done when using simulate_population() with sample_css = TRUE.

There are five options for the vary argument:

age: C$_{ss}$ values from the 'age' column of the 'fixed_css' table are used. For other cases, exposure rates are re-simulated using the median age by location in the 'sample' table by calling simulate_exposure_rate() with sensitivity = TRUE.
weight: C$_{ss}$ values from the 'weight' column of the 'fixed_css' table are used.
css_params: C$_{ss}$ values from the 'params' column of the 'fixed_css' table are used.
fit_params: C$_{ss}$ values from the 'other' column of the 'fixed_css' table are used. For other cases, the standard deviation of dose-response model fit parameters are set to zero by calling calc_risk() with fixed = TRUE.
C_ext: C$_{ss}$ values from the 'other' column of the 'fixed_css' table are used. For other cases, external concentrations are re-simulated with standard deviations set to zero by calling simulate_exposure() with sensitivity = TRUE.

In all cases above, the resulting risk table is named 'risk_sensitivity_~vary~' (e.g., 'risk_sensitivity_age') in the GeoTox database.

Inputs rate_extra_cols, expos_mean, and expos_sd do not need to be specified again if they were already provided in a previous call and are set in the GeoTox parameters (GT$par).

Examples

# Example setup is shown below in \dontrun().
# Pre-generated results will be loaded instead to avoid long example runtime.

if (FALSE) { # \dontrun{
# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)
set.seed(1234)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(
    hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
  )) |>
  simulate_population(exposure = exposure_df) |>
  calc_response()

# Calculate sensitivity to age
GT <- GT |> calc_sensitivity("age")
} # }

# Load results from pre-generated database for this example
temp_dir <- tempdir()
zip::unzip(
  system.file("extdata", "sensitivity.duckdb.zip", package = "GeoTox"),
  junkpaths = TRUE,
  exdir = temp_dir
)
GT <- GeoTox(paste0(temp_dir, "/sensitivity.duckdb"))

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant table

dplyr::tbl(con, "risk_sensitivity_age") |> dplyr::collect()
#> # A tibble: 6 × 6
#>   assay_id sample_id GCA.Eff IA.Eff GCA.HQ.10 IA.HQ.10
#>      <int>     <int>   <dbl>  <dbl>     <dbl>    <dbl>
#> 1        1         1    64.3   69.0      138.     138.
#> 2        1         2    64.8   69.5      162.     162.
#> 3        1         3    68.8   73.9      315.     315.
#> 4        2         1    39.4   39.4      202.     202.
#> 5        2         2    39.6   39.6      236.     236.
#> 6        2         3    40.5   40.5      633.     633.

# Compared to baseline risk table
dplyr::tbl(con, "risk") |> dplyr::collect()
#> # A tibble: 6 × 6
#>   assay_id sample_id GCA.Eff IA.Eff GCA.HQ.10 IA.HQ.10
#>      <int>     <int>   <dbl>  <dbl>     <dbl>    <dbl>
#> 1        1         1    62.0   68.2      59.0     59.0
#> 2        1         2    65.0   69.3     232.     232. 
#> 3        1         3    68.1   73.1     190.     190. 
#> 4        2         1    37.1   37.1      89.1     89.1
#> 5        2         2    38.9   38.9     395.     395. 
#> 6        2         3    38.1   38.1     393.     393. 

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)
#> [1] TRUE

Usage

Arguments

Value

Details

See also

Examples