Fixed troponin_precision dataset which was generated with constant values (zero variance) at each concentration level. The dataset now contains realistic variability following a hyperbolic CV model, enabling proper demonstration of precision_study() and precision_profile() functions.
Updated README with precision experiments examples.
precision_study(): Comprehensive variance component analysis for precision
experiments with nested experimental designs.
print(), summary(), plot(), autoplot()verify_precision(): Statistical verification of observed precision against
manufacturer claims using chi-square hypothesis testing.
precision_profile(): Models the relationship between CV and concentration
for functional sensitivity estimation.
The package now calculates and reports:
plot.precision_study() with three plot types:
type = "variance": Variance component bar charttype = "cv": CV profile across precision measures with CIstype = "precision": Forest plot of precision estimatesplot.precision_profile(): Publication-ready precision profile visualization
troponin_precision: High-sensitivity cardiac troponin I precision study data
with 6 concentration levels (5-500 ng/L), designed for demonstrating
precision_study() and precision_profile() workflows.lme4 added to Suggests for REML estimation (optional)Initial CRAN release.
ate_from_bv(): Calculate allowable total error (ATE) specifications from
biological variation data using the Fraser-Petersen model. Supports three
performance levels (optimal, desirable, minimum) and provides allowable
imprecision, allowable bias, and total allowable error specifications.
sigma_metric(): Calculate the Six Sigma metric for analytical performance
assessment. Returns sigma value with interpretation category (World Class
to Unacceptable) and approximate defect rates.
ate_assessment(): Comprehensive evaluation of observed method performance
against allowable total error specifications. Provides pass/fail assessment
for individual components (bias, CV, total error) and overall method
acceptability, integrated with sigma metric calculation.
deming_regression(): Deming regression for method comparison, accounting
for measurement error in both variables. Supports known error ratio or
estimation from replicates. Includes jackknife and bootstrap BCa confidence
intervals.
S3 methods for Deming regression: print(), summary(), plot(), and
autoplot() (ggplot2).
New vignette: "Deming Regression for Method Comparison" -- comprehensive guide to Deming regression theory and practical application.
Updated vignette: "Understanding Method Comparison Statistics" -- added guidance on choosing between regression methods.
ba_analysis(): Bland-Altman method comparison analysis with bias estimation,
limits of agreement, and confidence intervals. Supports both absolute and
percentage difference scaling.
pb_regression(): Passing-Bablok regression with fast O(n log n) algorithm
via the robslopes package. Includes analytical confidence intervals
(Passing & Bablok 1983) and optional bootstrap BCa intervals. CUSUM test
for linearity assessment with Kolmogorov-Smirnov p-value.
S3 methods for both analyses: print(), summary(), plot(), and
autoplot() (ggplot2).
Publication-ready visualizations using ggplot2, including Bland-Altman plots, regression scatter plots with confidence bands, residual plots, and CUSUM plots for linearity assessment.
glucose_methods: Point-of-care glucose meter vs laboratory analyzer (n=60)
creatinine_serum: Enzymatic vs Jaffe creatinine methods (n=80)
troponin_cardiac: Two high-sensitivity cardiac troponin I platforms (n=50)
Vignette: "Method Comparison Workflow" -- step-by-step analysis guide
Vignette: "Understanding Method Comparison Statistics" -- educational overview of statistical concepts for method comparison studies