Research Methodology¶
This section documents the research methodology underpinning every study published on this research center — the study designs, statistical methods, and validation frameworks that make our evidence citable and reproducible.
1. Study Design Framework¶
| Study Type | Purpose | Design |
|---|---|---|
| Analytical validation | Establish assay precision/accuracy | Spiked samples, replicates |
| Diagnostic accuracy | Sensitivity/specificity | Gold-standard comparison |
| Method comparison | Agreement vs reference | Bland-Altman, Passing-Bablok |
| Field evaluation | Real-world performance | Multi-site, clinical samples |
2. Statistical Methods¶
| Method | Application |
|---|---|
| ROC analysis | Determine optimal cutoff, AUC |
| Bland-Altman plot | Agreement between two methods |
| Passing-Bablok regression | Bias estimation (non-parametric) |
| CV% (coefficient of variation) | Precision (repeatability/reproducibility) |
| Confidence intervals (95%) | Precision of estimates |
3. Key Performance Metrics¶
| Metric | Formula | Target |
|---|---|---|
| Sensitivity | TP / (TP + FN) | > 90% |
| Specificity | TN / (TN + FP) | > 95% |
| Positive predictive value | TP / (TP + FP) | Context-dependent |
| Negative predictive value | TN / (TN + FN) | Context-dependent |
Sensitivity vs specificity — the trade-off: raising the cutoff increases specificity but lowers sensitivity, and vice versa. The optimal cutoff (via ROC analysis) balances both for the clinical context. A screening test prioritizes sensitivity (fewer missed cases); a confirmatory test prioritizes specificity (fewer false alarms).
4. The Validation Hierarchy¶
Migibio evaluates every assay through a three-level hierarchy:
- Analytical validation — does the assay measure the analyte accurately? (LOD, CV%, linearity)
- Clinical validation — does the assay detect the disease? (sensitivity/specificity vs gold standard)
- Utility validation — does the result change management? (outcome studies)
Migibio reports analytical and clinical validation for every assay; utility studies are ongoing.
5. Why Methodology Is Published¶
Publishing methodology is a trust signal: it lets a technically literate reader judge the evidence rather than take a headline claim at face value. It also enables reproducibility — a partner can verify our approach.
FAQ¶
Why report both Bland-Altman and Passing-Bablok? Bland-Altman visualizes agreement (bias and limits of agreement); Passing-Bablok estimates systematic and proportional bias non-parametrically. Together they give a complete picture of method agreement.
What is a good AUC for a diagnostic test? AUC (area under the ROC curve) above 0.9 is generally considered excellent, 0.8–0.9 good, and 0.7–0.8 fair.
Why does sensitivity/specificity not tell the whole story? These metrics ignore disease prevalence. In a rare disease, even a high-specificity test can produce many false positives — which is why predictive values (PPV/NPV) matter in clinical practice.
For detailed analytical validation, see Analytical Performance. For disease-specific accuracy, see Infectious Disease Research.
Authored by: Migibio Clinical & Scientific Affairs, Guangzhou Magic Biotech Co., Ltd.
Reviewed by: Migibio R&D Quality Committee
Last updated: 2026-08-13
Disclosure: Migibio (Guangzhou Magic Biotech Co., Ltd.) is the manufacturer of the FIA680/FIA880 analyzers and FICT reagents referenced in this content. See our Editorial & Review Policy.