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Verify Calibration Curve Performance

Source: R/calibration-fit.R
measure_calibration_verify.Rd

Evaluates the performance of a calibration curve using verification samples (continuing calibration verification - CCV, or independent QC samples). This function assesses whether the calibration remains valid during or between analytical runs.

Usage

measure_calibration_verify(
  calibration,
  verification_data,
  nominal_col = "nominal_conc",
  acceptance_pct = 15,
  acceptance_pct_lloq = 20,
  lloq = NULL,
  sample_type_col = NULL,
  criteria = NULL
)

Arguments

calibration

A measure_calibration object from measure_calibration_fit().

verification_data

A data frame containing verification samples with known concentrations.

nominal_col

Name of the column containing nominal (known) concentrations. Default is "nominal_conc".

acceptance_pct

Acceptance criterion as percent deviation from nominal. Default is 15 (i.e., ±15%).

acceptance_pct_lloq

Acceptance criterion for samples at the lower limit of quantitation (LLOQ). Default is 20 (i.e., ±20%).

lloq

Lower limit of quantitation. Samples at or near this level use acceptance_pct_lloq. Default is NULL (use same criterion for all).

sample_type_col

Optional column indicating sample types. Only samples with type containing "qc" or "ccv" will be used if specified.

criteria

Optional measure_criteria object for custom acceptance criteria. If provided, overrides acceptance_pct settings.

Value

A measure_calibration_verify object (a tibble) containing:

  • Predicted concentrations

  • Accuracy (%nominal)

  • Deviation from nominal (%)

  • Pass/fail status for each sample

  • Overall verification status

Details

Verification Workflow

Calibration verification is typically performed:

  1. At the beginning and end of analytical batches

  2. After every N unknown samples (e.g., every 10)

  3. When instrument performance is in question

Acceptance Criteria

Default criteria are based on bioanalytical guidelines:

  • Standard samples: ±15% of nominal

  • LLOQ samples: ±20% of nominal

For more stringent applications (e.g., clinical chemistry), consider using ±10% or providing custom criteria.

See also

measure_calibration_fit() for fitting calibration curves, measure_calibration_predict() for prediction, measure_criteria() for custom acceptance criteria.

Examples

# Fit calibration
cal_data <- data.frame(
  nominal_conc = c(1, 5, 10, 50, 100, 500),
  response = c(1.2, 5.8, 11.3, 52.1, 105.2, 498.7)
)
cal <- measure_calibration_fit(cal_data, response ~ nominal_conc)

# Verify with QC samples
qc_data <- data.frame(
  sample_id = c("QC_Low", "QC_Mid", "QC_High"),
  nominal_conc = c(3, 75, 400),
  response = c(3.3, 77.2, 385.1)
)

verify_result <- measure_calibration_verify(cal, qc_data)
print(verify_result)
#> 
#> ── Calibration Verification ────────────────────────────────────────────────────
#> ✖ Overall: FAIL (1/3 samples out of specification)
#> 
#> ── Sample Results ──
#> 
#> # A tibble: 3 × 8
#>   sample_id nominal_conc response predicted_conc accuracy_pct deviation_pct
#>   <chr>            <dbl>    <dbl>          <dbl>        <dbl>         <dbl>
#> 1 QC_Low               3      3.3           1.38         45.9       -54.1  
#> 2 QC_Mid              75     77.2          75.6         101.          0.857
#> 3 QC_High            400    385.          385.           96.3        -3.73 
#> # ℹ 2 more variables: acceptance_limit <dbl>, pass <lgl>