Advanced Diagnostic Testing Analysis
Evaluate diagnostic test performance with precision using AI-powered statistical analysis. Calculate sensitivity, specificity, predictive values, and ROC curves effortlessly.
In healthcare, the accuracy of diagnostic tests can mean the difference between timely treatment and missed diagnoses. Whether you're validating a new blood test, comparing imaging techniques, or evaluating screening protocols, statistical rigor is non-negotiable.
Traditional statistical software often requires extensive training, coding expertise, or expensive licenses. Sourcetable changes this by bringing AI-powered diagnostic test analysis directly into a familiar spreadsheet interface. Simply upload your data and let AI guide you through sensitivity analysis, specificity calculations, and ROC curve generation.
Why Choose Sourcetable for Diagnostic Analysis
Instant Statistical Calculations
Generate sensitivity, specificity, PPV, and NPV automatically from your confusion matrix data. No manual formula writing required.
ROC Curve Visualization
Create publication-ready ROC curves with AUC calculations and confidence intervals. Export directly to presentations or publications.
Multi-Test Comparisons
Compare multiple diagnostic tests side-by-side with statistical significance testing and effect size calculations.
AI-Powered Insights
Get contextual recommendations for improving test performance and identifying optimal cutoff values.
Clinical Decision Support
Calculate likelihood ratios and post-test probabilities to inform clinical decision-making processes.
Regulatory Compliance
Generate reports that meet FDA and other regulatory standards for diagnostic test validation studies.
Ready to analyze your diagnostic data?
Diagnostic Test Analysis in 4 Simple Steps
Upload Your Data
Import diagnostic test results from CSV files, lab systems, or enter manually. Sourcetable accepts any format including true/false positives and raw test values.
Configure Analysis Parameters
Set your gold standard reference, define positive/negative criteria, and specify confidence levels. AI suggests optimal settings based on your data characteristics.
Generate Statistical Metrics
Automatically calculate sensitivity, specificity, predictive values, likelihood ratios, and diagnostic odds ratios with confidence intervals.
Visualize and Export Results
Create ROC curves, forest plots, and summary tables. Export publication-ready figures and comprehensive statistical reports.
Real-World Diagnostic Testing Scenarios
Example 1: Cardiac Biomarker Validation
A hospital laboratory developed a new cardiac troponin assay and needs to validate its performance against the current gold standard. The study included 500 patients presenting with chest pain.
• True Positives: 85 (elevated troponin, confirmed MI)
• False Positives: 12 (elevated troponin, no MI)
• True Negatives: 378 (normal troponin, no MI)
• False Negatives: 25 (normal troponin, confirmed MI)
Sourcetable Analysis Results: Sensitivity: 77.3% (95% CI: 68.9-84.4%), Specificity: 96.9% (95% CI: 94.7-98.4%), PPV: 87.6%, NPV: 93.8%. The ROC curve showed an AUC of 0.932, indicating excellent diagnostic performance.
Example 2: Imaging Protocol Comparison
A radiology department compared three different MRI protocols for detecting brain lesions in multiple sclerosis patients. Each protocol was evaluated against histopathological confirmation.
• Standard Protocol: Sensitivity 82%, Specificity 91%
• Enhanced Protocol: Sensitivity 89%, Specificity 88%
• AI-Assisted Protocol: Sensitivity 94%, Specificity 92%
Sourcetable's multi-test comparison feature revealed that the AI-assisted protocol had significantly better sensitivity (p < 0.001) while maintaining comparable specificity, leading to its adoption as the new standard.
Example 3: Screening Program Optimization
A public health organization needed to optimize cutoff values for a diabetes screening program using HbA1c levels. The goal was to maximize case detection while minimizing false positives.
Using Sourcetable's ROC analysis, they tested multiple cutoff values from 5.7% to 6.5% HbA1c. The optimal cutoff of 6.1% provided 91% sensitivity and 78% specificity, balancing detection rates with resource utilization for follow-up testing.
Healthcare Applications
Laboratory Test Validation
Validate new assays, compare analytical methods, and establish reference ranges. Calculate analytical sensitivity and specificity for regulatory submissions.
Medical Device Evaluation
Assess diagnostic accuracy of medical devices including imaging systems, point-of-care tests, and AI diagnostic tools.
Clinical Research Studies
Power calculations for diagnostic studies, interim analyses, and final statistical reporting for clinical trials and observational studies.
Quality Assurance Programs
Monitor ongoing test performance, identify drift in analytical systems, and maintain quality control standards.
Population Screening
Optimize screening programs by analyzing test characteristics across different populations and risk groups.
Biomarker Discovery
Evaluate potential biomarkers for diagnostic utility, including combination biomarker panels and machine learning approaches.
Frequently Asked Questions
What statistical methods does Sourcetable support for diagnostic testing?
Sourcetable supports comprehensive diagnostic test evaluation including sensitivity/specificity analysis, ROC curves with AUC calculation, likelihood ratios, predictive values, diagnostic odds ratios, and confidence intervals. We also provide multi-test comparisons with statistical significance testing.
Can I analyze paired diagnostic tests or multiple raters?
Yes, Sourcetable handles paired test comparisons using McNemar's test and can analyze inter-rater agreement using kappa statistics. You can compare multiple diagnostic methods simultaneously and assess concordance between different evaluators.
How does Sourcetable handle missing data in diagnostic studies?
Sourcetable provides multiple approaches for missing data including complete case analysis, multiple imputation, and sensitivity analyses. The AI assistant recommends the most appropriate method based on your data pattern and study design.
Can I generate reports for regulatory submissions?
Absolutely. Sourcetable generates comprehensive statistical reports with all required metrics, confidence intervals, and visualizations that meet FDA and other regulatory standards for diagnostic test validation studies.
What sample size do I need for reliable diagnostic test analysis?
Sample size requirements depend on your expected sensitivity/specificity and desired precision. Sourcetable includes power calculation tools to help determine adequate sample sizes before starting your study, and can perform post-hoc power analyses.
How do I interpret likelihood ratios and their clinical significance?
Sourcetable automatically calculates positive and negative likelihood ratios with clinical interpretation guides. Values >10 or <0.1 indicate strong diagnostic evidence, while values 2-10 or 0.1-0.5 provide moderate evidence. The AI assistant provides context-specific interpretations.
Frequently Asked Questions
If you question is not covered here, you can contact our team.
Contact UsHow do I analyze data?
What data sources are supported?
What data science tools are available?
Can I analyze spreadsheets with multiple tabs?
Can I generate data visualizations?
What is the maximum file size?
Is this free?
Is there a discount for students, professors, or teachers?
Is Sourcetable programmable?
Ready to revolutionize your diagnostic analysis?
Join healthcare professionals using Sourcetable for advanced medical statistics and diagnostic test evaluation.