| troponin_precision | R Documentation |
Synthetic dataset for evaluating precision of a high-sensitivity cardiac troponin I (hs-cTnI) assay across multiple concentration levels. The data is designed for demonstrating precision studies with variance component analysis and precision profile estimation.
troponin_precision
A data frame with 120 rows and 6 variables:
Concentration level factor (L1-L6)
Day of measurement (D1-D5)
Run within day (R1-R2)
Replicate within run (1-2)
Measured concentration (ng/L)
Nominal target concentration (ng/L)
This synthetic dataset simulates a multi-level precision study for a high-sensitivity cardiac troponin I assay. The data characteristics:
Concentration levels: 5, 10, 25, 50, 100, 500 ng/L
Design: 5 days x 2 runs x 2 replicates per level (EP05-style)
Total observations: 120 (6 levels x 20 measurements each)
Precision pattern: CV decreases with concentration following a hyperbolic relationship
The precision profile follows the model:
CV = \sqrt{a^2 + (b/x)^2}
where approximately:
a \approx 3\% (asymptotic CV at high concentrations)
b \approx 25 (concentration-dependent component)
This gives expected CVs of approximately:
5 ng/L: ~5.5%
10 ng/L: ~3.5%
25 ng/L: ~3.0%
50 ng/L: ~2.0%
100 ng/L: ~2.0%
500 ng/L: ~3.5%
High-sensitivity cardiac troponin assays are used to diagnose acute myocardial infarction (AMI). Key clinical decision points include:
99th percentile upper reference limit (URL): typically 14-26 ng/L
Functional sensitivity (CV <= 10%): should be <= 50% of 99th percentile
Precision at low concentrations is critical for early AMI detection
Synthetic data generated to mimic realistic precision patterns. See
data-raw/make_troponin_precision.R for the generation script.
precision_study() for variance component analysis,
precision_profile() for CV-concentration modeling,
glucose_methods, creatinine_serum, troponin_cardiac
# Load the dataset
data(troponin_precision)
head(troponin_precision)
# Precision study with multiple concentration levels
prec <- precision_study(
data = troponin_precision,
value = "value",
sample = "level",
day = "day",
run = "run"
)
print(prec)
# Results for each concentration level
names(prec$by_sample)
# Generate precision profile
profile <- precision_profile(prec, cv_targets = c(10, 20))
print(profile)
plot(profile)
# Functional sensitivity at 10% CV
profile$functional_sensitivity
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