title: “Inflammatory age (iAge proxy)” output: rmarkdown::html_vignette —

Scope

Compute a simplified inflammatory age proxy (iAge) as a weighted sum of CRP, IL6, and TNFa. Supports NA policies, high-missingness warnings, and optional extreme scanning (warn/cap/NA/error). Units assumed: CRP mg/L; IL6, TNFa pg/mL (no internal conversions).

Load packages and demo data

Synthetic data with one missing value to show NA handling.

library(HealthMarkers)
library(tibble)

df <- tibble::tibble(
  CRP  = c(1.2, 3.5, NA),  # mg/L
  IL6  = c(2.0, 4.1, 1.5), # pg/mL
  TNFa = c(1.0, 1.8, 0.9)  # pg/mL
)

Column map (required)

Map each marker to your column names; all three are mandatory.

col_map <- list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa")

Core calculation

Default behavior (na_action = "omit"/ignore/warn): NAs are ignored in the weighted sum (treated as 0 contribution), preserving row count.

ia_default <- iAge(
  data = df,
  col_map = col_map,
  na_action = "omit",
  check_extreme = FALSE,
  verbose = FALSE
)

ia_default
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1 1.40 
#> 2 3.12 
#> 3 0.801

Keep NA vs drop

na_action = "keep" returns NA if any required marker is missing in a row. na_action = "error" stops on missing required inputs.

ia_keep <- iAge(
  data = df,
  col_map = col_map,
  na_action = "keep",
  check_extreme = FALSE
)

ia_keep
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA

Extreme screening

Scan for out-of-range inputs and cap into allowed bounds.

df_ext <- df
df_ext$CRP[2] <- 500  # extreme on purpose

ia_cap <- iAge(
  data = df_ext,
  col_map = col_map,
  check_extreme = TRUE,
  extreme_action = "cap",
  verbose = TRUE
)

ia_cap
#> # A tibble: 3 × 1
#>     iAge
#>    <dbl>
#> 1   1.40
#> 2 101.  
#> 3  NA

extreme_action = "warn" would only warn; error aborts; NA blanks flagged values before computing.

Weights

• Default weights: CRP 0.33, IL6 0.33, TNFa 0.34 (sum to 1). Named weights are reordered to CRP/IL6/TNFa.
• For cohorts where one marker is more informative, adjust weights (ensure they sum to 1):

iAge(df, col_map = col_map, weights = c(CRP = 0.5, IL6 = 0.25, TNFa = 0.25))
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.35
#> 2  3.22
#> 3 NA

Expectations

• All three markers must be present and numeric; coercion to numeric will error if NAs are introduced.
• na_action behaviors:
  • omit/ignore/warn: missing markers contribute 0 to the sum; rows preserved.
  • keep: any missing required marker yields NA for that row.
  • error: abort if any required marker is missing/NA.
• Extreme scan uses broad defaults (CRP 0–300, IL6 0–1000, TNFa 0–2000); adjust via extreme_rules for your lab ranges.
• Output: one-column tibble iAge; rows match input unless you subset beforehand.

Verbose diagnostics

Set verbose = TRUE (and healthmarkers.verbose = "inform") to surface three structured messages on each call: preparing inputs, the column map, and a results summary.

old_opt <- options(healthmarkers.verbose = "inform")
df_v <- tibble::tibble(CRP = 1.2, IL6 = 2.0, TNFa = 1.0)
iAge(df_v, col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa"), verbose = TRUE)
#> iAge(): preparing inputs
#> iAge(): column map: CRP -> 'CRP', IL6 -> 'IL6', TNFa -> 'TNFa'
#> iAge(): results: iAge 1/1
#> # A tibble: 1 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
options(old_opt)

Tips

• Keep units consistent (CRP mg/L; IL6/TNFa pg/mL). Convert before calling if needed.
• Prefer na_action = "keep" when you want to see which rows are incomplete; use omit when you want continuity of the score despite single missing markers.
• Tighten extreme_rules for cleaner QA; use verbose = TRUE during setup to see mapping and warnings.
• Impute upstream (e.g., impute_missing()) if you prefer not to down-weight missing markers to zero.