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Validate Laboratory Non-Detect Imputation Results

Source: R/validate_imputation.R
validate_imputation.Rd

This function validates the quality of non-detect value imputation by checking that imputed values are below their respective limits of quantification and providing comprehensive summary statistics and model diagnostics.

Usage

validate_imputation(
  dt_imputed,
  value_col = "value",
  cens_col = "censored",
  verbose = TRUE
)

Arguments

dt_imputed

A data.table returned from impute_nondetect

value_col

Character string specifying the column name containing original values

cens_col

Character string specifying the column name containing censoring indicators

verbose

Logical indicating whether to print validation results to console (default: TRUE)

Value

Invisibly returns the input data.table. When verbose = TRUE, prints validation results to console including:

  • Whether all imputed values are below their detection limits

  • Number of duplicate imputed values (if any)

  • Summary statistics by detection limit level

  • Model fit information

Details

The function checks:

  • All imputed values are strictly below their respective limits of quantification

  • Uniqueness of imputed values

  • Summary statistics by limits of quantification level

  • Model fit information including parameter and unit details

  • Dataset characteristics (sample size, censoring percentage)

Examples

data(multi_censored_data)
result <- impute_nondetect(multi_censored_data, verbose = FALSE)
validate_imputation(result)
#> === Laboratory Non-Detect Imputation Validation ===
#> Sample size: 200
#> Censoring percentage: 16.5 %
#> ✓ All imputed values are strictly below their respective detection limits
#> ✓ All imputed values are unique
#> 
#> Summary by limit of quantification level:
#>      loq count min_imputed max_imputed mean_imputed
#>    <num> <int>       <num>       <num>        <num>
#> 1:     5    10    1.714617    4.903044     3.373300
#> 2:     8     8    4.146878    7.799514     6.466325
#> 3:    15    12    3.755751   11.857531     8.251673
#> 4:    25     3   13.599483   22.597835    18.331666
#> Total non-detect observations: 33
#> Best distribution: lognormal
#> Model AIC: 1220.47
#> Detection limits found: 5, 8, 15, 25

# Silent validation for batch processing
validate_imputation(result, verbose = FALSE)