Comprehensive analytical method validation following CLSI EP15-A3, EP05-A3, and ISO 15189 guidelines. Validates precision, accuracy, linearity, measuring interval, and analytical measurement range for clinical laboratory methods. Supports regulatory submissions and laboratory accreditation. Essential for establishing method performance characteristics.
Usage
methodvalidation(
data,
measurement,
reference_value,
replicate,
concentration_level,
operator,
instrument,
validation_type = "full_validation",
precision_design = "ep15a3",
replicates_per_run = 3,
number_of_runs = 20,
confidence_level = 0.95,
acceptance_criteria = "biological_variation",
custom_cv_limit = 10,
custom_bias_limit = 5,
linearity_points = 7,
outlier_detection = TRUE,
precision_analysis = TRUE,
accuracy_analysis = TRUE,
linearity_analysis = TRUE,
limit_of_detection = FALSE,
limit_of_quantitation = FALSE,
interference_testing = FALSE,
carryover_assessment = FALSE,
reference_interval = FALSE,
uncertainty_budget = TRUE,
validation_plots = TRUE,
method_comparison_plot = TRUE,
export_report = FALSE
)Arguments
- data
the data as a data frame
- measurement
Test method measurement values
- reference_value
Reference method or target concentration values
- replicate
Replicate measurement identifier
- concentration_level
Concentration level identifier (e.g., Low, Medium, High)
- operator
Operator or analyst identifier for intermediate precision
- instrument
Instrument identifier for ruggedness testing
- validation_type
Type of validation study to perform
- precision_design
Experimental design for precision evaluation
- replicates_per_run
Number of replicates per run for precision study
- number_of_runs
Number of runs for precision study
- confidence_level
Confidence level for statistical calculations
- acceptance_criteria
Source for method acceptance criteria
- custom_cv_limit
Custom coefficient of variation acceptance limit
- custom_bias_limit
Custom bias acceptance limit
- linearity_points
Number of concentration points for linearity study
- outlier_detection
Apply statistical outlier detection methods
- precision_analysis
Perform comprehensive precision evaluation
- accuracy_analysis
Perform bias and accuracy assessment
- linearity_analysis
Perform linearity and measuring interval evaluation
- limit_of_detection
Estimate analytical limit of detection
- limit_of_quantitation
Estimate analytical limit of quantitation
- interference_testing
Evaluate potential analytical interferences
- carryover_assessment
Assess sample carryover effects
- reference_interval
Verify established reference intervals
- uncertainty_budget
Calculate comprehensive uncertainty budget
- validation_plots
Generate validation study visualizations
- method_comparison_plot
Create method comparison visualizations
- export_report
Generate comprehensive validation report
Value
A results object containing:
results$instructions | a html | ||||
results$studyDesign | a table | ||||
results$precisionResults | a table | ||||
results$accuracyResults | a table | ||||
results$linearityResults | a table | ||||
results$linearityData | a table | ||||
results$detectionLimits | a table | ||||
results$uncertaintyBudget | a table | ||||
results$uncertaintySummary | a table | ||||
results$validationSummary | a table | ||||
results$interferenceResults | a table | ||||
results$carryoverResults | a table | ||||
results$precisionPlot | an image | ||||
results$accuracyPlot | an image | ||||
results$linearityPlot | an image | ||||
results$methodComparisonPlot | an image | ||||
results$uncertaintyPlot | an image | ||||
results$methodExplanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$studyDesign$asDF
as.data.frame(results$studyDesign)