PreciMind QC-IS
Quality Control Intelligence System — validated statistical QC core with exact single-rule probability calculations and candidate procedure comparison.
Input & Computation
Validated Statistical Core
QC-IS uses exact analytical probability calculations for validated single rules (13s, 12.5s) with N = 1–4. Operational multirule planning, ARL, and frequency recommendations are outside the current validated core.
Layer 1: Data Input
Enter the analyte, method, and analytical performance inputs used for the calculation.
Use a representative long-term within-laboratory CV derived from stable routine IQC data. Formal precision evaluation or verification should follow the applicable current CLSI EP05/EP15 guidance.
Enter signed bias (%). Negative values indicate underestimation. The interpretation of bias depends on the target source; peer-group EQA and reference-method/CRM targets are not equivalent estimates of trueness.
Enter the allowable-error specification used for the Sigma calculation. The selected specification should be appropriate to the analyte, method, concentration, and intended analytical-performance framework.
Descriptive laboratory context only. The current exact single-rule model does not use the number of control levels to calculate P_fr or P_edc.
N is the total number of control measurements available when the QC decision is made. It is not necessarily the number of QC levels.
Interpretation Guide
Sigma Formula
σ = (TEa − |Bias|) ÷ CV
Conventional Statistical Targets
- P_edc ≥ 0.90 — conventional error detection target (not a regulatory requirement)
- P_fr ≤ 0.05 — conventional false rejection target
These are conventional planning targets, not regulatory or accreditation requirements.
Evidence & References
Walker BS, Pearson LN, Schmidt RL (2020)
An Analysis of Multirules for Monitoring Assay Quality Control. Laboratory Medicine 51(1):94–98. DOI: 10.1093/labmed/lmz038
Bayat H, Westgard SA, Westgard JO (2017)
Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New CLSI C24-Ed4 Guidance. J Appl Lab Med 2(2):211–221. DOI: 10.1373/jalm.2017.023192
CLSI C24, 4th Edition (2016)
Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions.
CLSI EP05, 4th Edition (2025)
Evaluation of Precision of Quantitative Measurement Procedures.
ISO 15189:2022 §7.3.7.2
Internal quality control.
QC-IS operates as a validated statistical pipeline: Data Input → Analytical Engine → Statistical Core (exact single-rule probabilities) → Analytical Interpretation → Scope & Limitations. The validated core computes P_fr, P_ed, and P_edc using exact analytical functions. Multirule operational planning, ARL, and frequency recommendations are deferred.
PM Lab Suite
Clinical Laboratory Intelligence Platform
When to Use
- IQC strategy design for quantitative methods
- Method validation after reagent/calibrator lot changes
- Statistical QC planning with exact probability calculations
- Teaching QC intelligence principles in laboratory medicine education
Common Pitfalls
- Using within-run CV instead of long-term between-run CV — use a representative within-laboratory CV from stable routine IQC data
- Applying CLIA TEa limits as clinical targets — CLIA PT criteria are US proficiency-testing acceptable-performance criteria
- Ignoring bias in sigma calculation — even 1–2% bias significantly reduces sigma
- Treating sigma as a stable property — it changes with reagent lot, calibration, and instrument state