decisioncompare Indeterminate Results Data
Source:R/data_decisioncompare_docs.R
decisioncompare_indeterminate.RdDataset with 170 patients where Test1 includes indeterminate results (three levels) alongside standard binary Test2.
Format
A data frame with 170 rows and 4 variables:
- patient_id
Character: Patient identifier (PT001-PT170)
- GoldStandard
Factor: True status ("Negative", "Positive"), 30% positive
- Test1
Factor: Test with indeterminate ("Negative", "Positive", "Indeterminate")
- Test2
Factor: Standard binary test ("Negative", "Positive"), Sens=0.85, Spec=0.88
- age
Numeric: Patient age in years (mean 55, SD 13)
Details
Demonstrates handling of indeterminate results. Test1 has ~10% indeterminate results in both disease groups. Use excludeIndeterminate option to control handling.
Examples
data(decisioncompare_indeterminate)
decisioncompare(data = decisioncompare_indeterminate, gold = "GoldStandard",
goldPositive = "Positive", goldNegative = NULL, test1 = "Test1",
test1Positive = "Positive", test1Negative = NULL, test2 = "Test2",
test2Positive = "Positive", test2Negative = NULL,
test3Positive = "", test3Negative = NULL,
excludeIndeterminate = TRUE)
#>
#> COMPARE MEDICAL DECISION TESTS
#>
#> Test 1 - Recoded Data
#> ────────────────────────────────────────────────────────────────
#> Gold Positive Gold Negative Total
#> ────────────────────────────────────────────────────────────────
#> Test Positive 41.000000 14.00000 55.00000
#> Test Negative 5.000000 110.00000 115.00000
#> Total 46.000000 124.00000 170.00000
#> ────────────────────────────────────────────────────────────────
#>
#>
#> Test 2 - Recoded Data
#> ────────────────────────────────────────────────────────────────
#> Gold Positive Gold Negative Total
#> ────────────────────────────────────────────────────────────────
#> Test Positive 40.000000 13.00000 53.00000
#> Test Negative 6.000000 111.00000 117.00000
#> Total 46.000000 124.00000 170.00000
#> ────────────────────────────────────────────────────────────────
#>
#>
#> Decision Test Comparison
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Sensitivity Specificity Accuracy Positive Predictive Value Negative Predictive Value Positive Likelihood Ratio Negative Likelihood Ratio
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test1 89.13043 88.70968 88.82353 74.54545 95.65217 7.894410 0.1225296
#> → Good balanced performance; Moderate positive evidence; Moderate negative evidence
#> Test2 86.95652 89.51613 88.82353 75.47170 94.87179 8.294314 0.1457109
#> → Good balanced performance; Moderate positive evidence; Moderate negative evidence
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="font-family: Arial, sans-serif; max-width: 800px; margin:
#> 0 auto; padding: 20px;"><h2 style="color: #2c3e50; border-bottom: 2px
#> solid #3498db;"> Clinical Summary
#>
#> Among the tests evaluated, Test1 demonstrated optimal diagnostic
#> performance, with 89.1% sensitivity (95% CI: 76.4-96.4%), 88.7%
#> specificity (95% CI: 81.8-93.7%), 74.5% positive predictive value,
#> 95.7% negative predictive value, and 88.8% overall accuracy. The
#> likelihood ratio for positive results was 7.89 and for negative
#> results was 0.12.<h3 style="color: #27ae60; margin-top: 30px;"> Report
#> Sentences
#>
#> <div style="background-color: #f8f9fa; padding: 15px; border-left: 4px
#> solid #28a745; margin: 15px 0;"><h4 style="margin-top: 0;">Methods
#> Section:
#>
#> <p style="font-style: italic; line-height: 1.6;">We compared the
#> diagnostic performance of 2 tests (Test1, Test2) against the gold
#> standard reference using diagnostic accuracy analysis. The study
#> included 170 cases with complete data. Performance metrics calculated
#> included sensitivity, specificity, positive and negative predictive
#> values, likelihood ratios, and overall accuracy.
#>
#> <div style="background-color: #e8f4f8; padding: 15px; border-left: 4px
#> solid #3498db; margin: 15px 0;"><h4 style="margin-top: 0;">Results
#> Section:
#>
#> <p style="font-style: italic; line-height: 1.6;">Among the tests
#> evaluated, Test1 demonstrated optimal diagnostic performance, with
#> 89.1% sensitivity (95% CI: 76.4-96.4%), 88.7% specificity (95% CI:
#> 81.8-93.7%), 74.5% positive predictive value, 95.7% negative
#> predictive value, and 88.8% overall accuracy. The likelihood ratio for
#> positive results was 7.89 and for negative results was 0.12.
#>
#> <h3 style="color: #8e44ad; margin-top: 30px;"> Clinical
#> Recommendations
#>
#> <div style="background-color: #fff3cd; padding: 15px; border-radius:
#> 8px;">
#>
#> Clinical Consideration: Consider using Test1 in combination with other
#> tests for optimal diagnostic accuracy.
#>
#> Implementation Note: Results should be interpreted in the context of
#> disease prevalence in your clinical population. Consider local
#> validation studies before implementation.
#>
#> <div style="font-family: Arial, sans-serif; max-width: 900px; margin:
#> 0 auto; padding: 20px;"><h2 style="color: #2c3e50; text-align: center;
#> border-bottom: 2px solid #3498db; padding-bottom: 10px;"> About
#> Medical Decision Test Comparison
#>
#> <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb
#> 100%); padding: 20px; border-radius: 10px; margin: 20px 0;"><h3
#> style="color: #1565c0; margin-top: 0;"> What This Analysis Does
#>
#> <p style="line-height: 1.6; color: #333;">This tool compares the
#> diagnostic performance of multiple medical tests against a gold
#> standard reference. It systematically evaluates sensitivity,
#> specificity, predictive values, likelihood ratios, and overall
#> accuracy to help you determine which test performs best for your
#> clinical scenario.
#>
#> <div style="background-color: #f1f8e9; border: 1px solid #8bc34a;
#> padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
#> #4a7c59; margin-top: 0;"> When to Use This Analysis
#>
#> <ul style="line-height: 1.8; color: #4a7c59;">Test Validation:
#> Comparing new diagnostic methods against established standardsMethod
#> Comparison: Evaluating which of several tests performs betterClinical
#> Research: Validating biomarkers, imaging techniques, or clinical
#> assessmentsQuality Assessment: Measuring agreement between different
#> raters or methodsProtocol Development: Optimizing diagnostic
#> workflows<div style="background-color: #fff3e0; border: 1px solid
#> #ff9800; padding: 20px; border-radius: 8px; margin: 20px 0;"><h3
#> style="color: #e65100; margin-top: 0;"> How to Use This Analysis
#>
#> <ol style="line-height: 1.8; color: #e65100;">Select Gold Standard:
#> Choose your most reliable reference test (e.g., biopsy, expert
#> consensus)Choose Tests to Compare: Select 2-3 diagnostic tests you
#> want to evaluateDefine Positive Levels: Specify what constitutes a
#> "positive" result for each testConfigure Options: Enable statistical
#> comparisons, confidence intervals, or visualizations as neededRun
#> Analysis: Review results tables and clinical interpretationsCopy
#> Report: Use the auto-generated sentences for your documentation<div
#> style="background-color: #f3e5f5; border: 1px solid #9c27b0; padding:
#> 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color: #6a1b9a;
#> margin-top: 0;"> Key Metrics Explained
#>
#> <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
#> color: #6a1b9a;">
#>
#> Sensitivity: Probability test is positive when disease present
#> (rule-out ability)
#>
#> Specificity: Probability test is negative when disease absent (rule-in
#> ability)
#>
#> PPV: Probability of disease when test positive
#>
#> NPV: Probability of no disease when test negative
#>
#> LR+: How much positive test increases odds of disease
#>
#> LR-: How much negative test decreases odds of disease
#>
#> Accuracy: Overall probability of correct classification
#>
#> McNemar Test: Statistical comparison between paired tests
#>
#> <div style="background-color: #e8f5e8; border: 1px solid #4caf50;
#> padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
#> #2e7d32; margin-top: 0;"> Clinical Interpretation Guidelines
#>
#> <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px;
#> color: #2e7d32;"><h4 style="margin-bottom: 5px;">Screening Tests
#> (Rule-Out):
#>
#> <p style="margin-top: 0;">• Sensitivity >=95%: Excellent
#> • NPV >=95%: High confidence
#> • Goal: Minimize false negatives
#>
#> <h4 style="margin-bottom: 5px;">Confirmatory Tests (Rule-In):
#>
#> <p style="margin-top: 0;">• Specificity >=95%: Excellent
#> • PPV >=90%: High confidence
#> • Goal: Minimize false positives
#>
#> <div style="background-color: #fff8e1; border: 1px solid #ffc107;
#> padding: 20px; border-radius: 8px; margin: 20px 0;"><h3 style="color:
#> #f57f17; margin-top: 0;"> Important Assumptions & Limitations
#>
#> <ul style="line-height: 1.6; color: #f57f17;">Gold Standard: Assumes
#> your reference test is truly accurateSample Size: Results more
#> reliable with larger, representative samplesPrevalence Dependency: PPV
#> and NPV vary with disease prevalenceMcNemar Test: Requires
#> paired/matched data for statistical comparisonsMissing Data: Cases
#> with incomplete data are excluded from analysisConfidence Intervals:
#> The per-test CI tables report Clopper-Pearson exact intervals for the
#> proportions (sensitivity, specificity, PPV, NPV, accuracy and
#> prevalence), as computed by epiR::epi.tests() with its default
#> settings. Likelihood ratios are reported as point estimates only,
#> without confidence intervals. The Overall Percent Agreement (OPA)
#> table uses the method you select under "CI Method for Agreement"
#> (Wilson score by default). Paired differences between tests use
#> normal-approximation (Wald) intervals.
#>
#> <div style='margin: 10px 0;'><div style='background-color: #fff7ed;
#> border-left: 4px solid #fdba74; padding: 12px; margin: 8px 0;
#> border-radius: 4px;'><strong style='color: #ea580c;'> Multi-Level Test
#> Variable
#> <span style='color: #374151;'>Test1 has 3 levels: Negative, Positive,
#> Indeterminate. Only "Positive" treated as positive; all
#> others (Negative, Indeterminate) treated as NEGATIVE. This may inflate
#> specificity/NPV if equivocal results are present. 115
#> non-positive cases detected. Consider enabling "Exclude
#> Indeterminate" option or using binary variables.<div
#> style='background-color: #fff7ed; border-left: 4px solid #fdba74;
#> padding: 12px; margin: 8px 0; border-radius: 4px;'><strong
#> style='color: #ea580c;'> Cannot Exclude Indeterminate Levels
#> Automatically
#> <span style='color: #374151;'>"Exclude
#> indeterminate/Equivocal levels" is enabled, but no negative
#> level has been selected for Test1, which has more than two levels. The
#> analysis cannot tell which non-positive level is a genuine negative
#> and which is equivocal, so no rows were excluded for it: every
#> non-positive value is still being counted as Negative, which inflates
#> specificity and NPV. Select the negative level for that variable.<div
#> style='background-color: #eff6ff; border-left: 4px solid #93c5fd;
#> padding: 12px; margin: 8px 0; border-radius: 4px;'><strong
#> style='color: #2563eb;'> Analysis Completed Successfully
#> <span style='color: #374151;'>2 diagnostic tests compared using 170
#> complete cases. Gold standard identified 46 diseased and 124 healthy
#> cases. Review comparison tables and statistical tests below.