Analysis of diagnostic tests without a gold standard reference using multiple statistical approaches. Implements Latent Class Analysis (Hui & Walter, 1980), Bayesian methods (Joseph et al., 1995), and composite reference standards for estimating test performance when no perfect reference test exists.
Usage
nogoldstandard(
data,
clinicalPreset = "none",
test1,
test1Positive,
test2 = NULL,
test2Positive,
test3 = NULL,
test3Positive,
test4 = NULL,
test4Positive,
test5 = NULL,
test5Positive,
method = "latent_class",
bootstrap = FALSE,
nboot = 1000,
alpha = 0.05,
verbose = FALSE,
seed = 0
)Arguments
- data
The data as a data frame.
- clinicalPreset
Predefined clinical scenarios with optimized settings and method recommendations.
- test1
First diagnostic test variable.
- test1Positive
The positive level for Test 1.
- test2
Second diagnostic test variable.
- test2Positive
The positive level for Test 2.
- test3
Third diagnostic test variable (optional).
- test3Positive
The positive level for Test 3.
- test4
Fourth diagnostic test variable (optional).
- test4Positive
The positive level for Test 4.
- test5
Fifth diagnostic test variable (optional).
- test5Positive
The positive level for Test 5.
- method
Method for analyzing tests without gold standard.
- bootstrap
Calculate bootstrap confidence intervals.
- nboot
Number of bootstrap samples for confidence intervals.
- alpha
Alpha level for confidence intervals.
- verbose
Show detailed progress messages during bootstrap analysis.
- seed
Base random seed for the reproducible latent-class multi-start search. Each start is offset from this base, so changing it shifts the whole reproducible sequence; the default (0) reproduces the previous behaviour.
Value
A results object containing:
results$notices | a preformatted | ||||
results$instructions | a html | ||||
results$agreement_stats | a table | ||||
results$clinical_summary | a html | ||||
results$method_guide | a html | ||||
results$prevalence | a table | ||||
results$test_metrics | a table | ||||
results$model_fit | a table | ||||
results$conditional_dependence | Latent class analysis assumes the tests err independently given true disease status. For each pair this compares the observed two-way table with the one the fitted model implies; a residual above 3.84 (the 5 percent point of chi-squared on 1 degree of freedom) is evidence that the pair shares a source of error, which inflates the estimated accuracy. Requires four or more tests: with three the model is just-identified and reproduces every table exactly, so no residual can reveal dependence. | ||||
results$diagnostics | Detail of how the estimates were produced: sample size, method, convergence, number of random starts used, and bootstrap failures. Shown only when Verbose output is enabled. | ||||
results$crosstab | a table | ||||
results$agreement_plot | an image | ||||
results$agreement_plot2 | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$agreement_stats$asDF
as.data.frame(results$agreement_stats)