Systematic evaluation of diagnostic test combinations. Analyzes all possible test result patterns (2-test: 4 patterns, 3-test: 8 patterns) against a gold standard to identify optimal testing strategies. Calculates sensitivity, specificity, PPV, NPV, and accuracy for each pattern combination.
Usage
decisioncombine(
data,
gold,
goldPositive,
test1,
test1Positive,
test2,
test2Positive,
test3 = NULL,
test3Positive,
showIndividual = FALSE,
showFrequency = FALSE,
showBarPlot = FALSE,
showHeatmap = FALSE,
showForest = FALSE,
showDecisionTree = FALSE,
showRecommendation = FALSE,
addPatternToData = FALSE,
filterStatistic = "all",
filterPattern = "all"
)Arguments
- data
The data as a data frame.
- gold
The gold standard reference variable representing true disease status.
- goldPositive
The level indicating presence of disease in the gold standard.
- test1
The first diagnostic test variable.
- test1Positive
The level representing a positive result for Test 1.
- test2
The second diagnostic test variable for combination analysis.
- test2Positive
The level representing a positive result for Test 2.
- test3
The third diagnostic test variable for 3-way combination analysis.
- test3Positive
The level representing a positive result for Test 3.
- showIndividual
Boolean to show individual test performance tables.
- showFrequency
Boolean to show frequency tables.
- showBarPlot
Boolean to display bar chart visualization.
- showHeatmap
Boolean to display heatmap visualization.
- showForest
Boolean to display forest plot.
- showDecisionTree
Boolean to display decision tree visualization.
- showRecommendation
Boolean to show optimal pattern recommendation table.
- addPatternToData
Boolean to add test pattern column to the dataset.
- filterStatistic
Character indicating which statistic to display (default: all).
- filterPattern
Character indicating which pattern type to display (default: all).
Value
A results object containing:
results$combinationTable | Counts and diagnostic performance metrics for each test combination pattern and clinical strategy, including prevalence, balanced accuracy, Youden's J, likelihood ratios, and diagnostic odds ratios | ||||
results$combinationTableCI | 95\
results$goldFreqTable | ||||
| Frequency distribution of the gold standard (reference) test showing counts and percentages for each level | results$crossTabTable | ||||
| Cross-tabulation showing how test combination patterns align with gold standard results | results$individualTest1$test1Contingency | ||||
| a table | results$individualTest1$test1Stats | ||||
| a table | results$individualTest2$test2Contingency | ||||
| a table | results$individualTest2$test2Stats | ||||
| a table | results$individualTest3$test3Contingency | ||||
| a table | results$individualTest3$test3Stats | ||||
| a table | results$barPlot | ||||
| Grouped bar chart comparing sensitivity, specificity, PPV, NPV, and accuracy across test combinations | results$heatmapPlot | ||||
| Color-coded heatmap showing all diagnostic metrics for each test pattern | results$forestPlot | ||||
Forest plot displaying 95\
results$decisionTreePlot | |||||
| Hierarchical decision tree showing test patterns with performance-based recommendations | results$recommendationTable |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$combinationTable$asDF
as.data.frame(results$combinationTable)
Examples
# Two-test combination analysis
result <- decisioncombine(
data = histopathology,
gold = "Golden Standart",
goldPositive = "1",
test1 = "New Test",
test1Positive = "1",
test2 = "Rater 1",
test2Positive = "1",
showIndividual = TRUE
)
#> Error in decisioncombine(data = histopathology, gold = "Golden Standart", goldPositive = "1", test1 = "New Test", test1Positive = "1", test2 = "Rater 1", test2Positive = "1", showIndividual = TRUE): argument "test3Positive" is missing, with no default