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Proficiency Testing Scores

Source: R/method-comparison.R
measure_proficiency_score.Rd

Calculates proficiency testing scores (z-scores, En scores, or zeta scores) for evaluating laboratory performance in interlaboratory comparisons.

Usage

measure_proficiency_score(
  data,
  measured_col,
  reference_col,
  uncertainty_col = NULL,
  reference_uncertainty_col = NULL,
  score_type = c("z_score", "en_score", "zeta_score"),
  sigma = NULL,
  group_col = NULL
)

Arguments

data

A data frame containing measurement data.

measured_col

Name of column with measured/reported values.

reference_col

Name of column with reference/assigned values.

uncertainty_col

Name of column with measurement uncertainties. Required for En and zeta scores.

reference_uncertainty_col

Name of column with reference value uncertainties. Optional for En/zeta scores.

score_type

Type of score to calculate:

  • "z_score" (default): (measured - reference) / sigma

  • "en_score": (measured - reference) / sqrt(U_meas^2 + U_ref^2)

  • "zeta_score": Similar to En, for correlated uncertainties

sigma

Standard deviation for z-score calculation. If NULL, estimated from the data.

group_col

Optional grouping column for separate assessments.

Value

A measure_proficiency_score object containing:

  • scores: Tibble with individual scores and flags

  • statistics: Summary statistics and counts

Details

Score Interpretation

| |Score| | Status | Action | |———|—————|——–| | <= 2 | Satisfactory | None | | 2-3 | Questionable | Review | | > 3 | Unsatisfactory| Investigate |

Score Types

z-score: Uses a fixed standard deviation (sigma), typically derived from historical data or consensus of participants.

En score: Uses expanded uncertainties of both the lab result and reference value. Appropriate when uncertainties are well-characterized.

zeta score: Similar to En, but accounts for potential correlation between lab and reference uncertainties.

See also

measure_accuracy(), criteria_proficiency_testing()

Other method-comparison: measure_bland_altman(), measure_deming_regression(), measure_passing_bablok()

Examples

# Proficiency testing results from multiple labs
pt_data <- data.frame(
  lab_id = paste0("Lab_", 1:10),
  measured = c(99.2, 100.5, 98.8, 101.2, 97.5, 100.1, 99.8, 102.3, 100.6, 94.0),
  assigned = rep(100, 10),
  uncertainty = c(1.5, 2.0, 1.8, 1.6, 2.2, 1.9, 1.7, 2.1, 1.5, 2.0)
)

# z-scores with known sigma
z_result <- measure_proficiency_score(
  pt_data,
  measured_col = "measured",
  reference_col = "assigned",
  score_type = "z_score",
  sigma = 2.5
)

print(z_result)
#> measure_proficiency_score
#> ──────────────────────────────────────────────────────────────────────────────── 
#> 
#> Score Type: z_score 
#> Sigma: 2.5 
#> 
#> Results (n = 10 ):
#>   Satisfactory (|z| ≤ 2): 9 ( 90 %)
#>   Questionable (2 < |z| ≤ 3): 1 
#>   Unsatisfactory (|z| > 3): 0 
#> 
#> Score Statistics:
#>   Mean score: -0.24 
#>   SD score: 0.925 
#>   Max |score|: 2.4 

# En scores using uncertainties
en_result <- measure_proficiency_score(
  pt_data,
  measured_col = "measured",
  reference_col = "assigned",
  uncertainty_col = "uncertainty",
  score_type = "en_score"
)

print(en_result)
#> measure_proficiency_score
#> ──────────────────────────────────────────────────────────────────────────────── 
#> 
#> Score Type: en_score 
#> 
#> Results (n = 10 ):
#>   Satisfactory (|z| ≤ 2): 9 ( 90 %)
#>   Questionable (2 < |z| ≤ 3): 1 
#>   Unsatisfactory (|z| > 3): 0 
#> 
#> Score Statistics:
#>   Mean score: -0.291 
#>   SD score: 1.16 
#>   Max |score|: 3