# Guest Essay

## Dietmar Stockl, QC Reality Check, Parts One and Two

**Dietmar Stockl, colleague, friend, and expert in statistics, starts a series on looking at QC data examples from the real world of the laboratory. Sometimes what happens in reality is different than what's predicted in theory.**

# Internal Quality Control - A Reality Check

- Part I: Setting the scene
- Part II: “Pearls on a rope”
- Part III: Purposely working with the “wrong target”
- Part IV: Purposely working with the “wrong standard deviation”
- Part V: How variable/stable do I want it?
- Part VI: How stable can I get it?
- Part VII: What’s going on? – 1

## Part I: Setting the scene

#### Dietmar Stockl, PhD

January 2011

### Remark

An analytical process has two major parts: i) the measurement procedure, which is necessary to obtain a result for a patient's sample; ii) the control procedure, which is necessary to assess the validity of a measurement result (1). This statement makes absolutely clear that IQC is a "sine-qua-non" for reporting a result. Thus, a well established IQC system is an important part of the technical competence of the laboratory.

### The analytical and IQC paradigms

*Analytical paradigm (2)*

Analytical procedures give results that are independent from other results; the results come from a Gaussian distribution with a mean µ and a standard deviation *"sigma"*. Analytical procedures have periods of stable performance; mean and standard deviation of the stable process can be estimated from sufficiently frequent measurements under stable conditions. However, in the course of time, analytical procedures tend to instability:

- Measured means deviate from the "true" mean due to the occurrence of systematic error
- Measured s is >"true
*sigma*" due to increased random error

*IQC-paradigm (2)*

IQC can detect process deterioration (increased systematic or random error) at a sufficiently early stage:

- By repeated measurement of the same sample and investigation of the results by statistical methods (control rules)

Control rules indicate, for example, whether

- The actual mean deviates from µ and/or the actual s is > "
*sigma*."

IQC uses a reliable estimate of µ and *"sigma"* for controlling the measurement process (for example, setting the control limits).

### References

- Westgard JO, Barry PL. Cost-effective quality control. AACC Press, 1995.
- www.stt-consulting.com >Education >IQC book I.

The ideal quality control situation IQC measurements of a stable control (no breakdown, evaporation, or contamination) for serum-sodium (Na, mmol/L).

Stable measurement process for Na, with “µ” = 145 mmo/L, *"sigma"* = 1.53 mmol/L (CV = 1.1%). Indicated are the mean, and the mean ± 1 x, 2 x, 3 x *“sigma”*.

Results are reported with 1 decimal.

If the control rule = 1_{3s}, we observe 1 FALSE rejection (probability = 0.27%).

## Part II: “Pearls on a rope”

When the Na-results are reported without decimal, we observe “ropes of pearls”.

Ropes of pearls are observed when (too much) rounding does not match precision.

### Reality check: TSH low level IQC sample

### CAVE

Too few decimals (digits) may give problems with target uncertainty (rounding problem), calculation of CV (zero CV, last 50 results in TSH-example), violation of IQC rules & graphical display.

### Solution

If the software allows it, use more digits/decimals for IQC purposes than you use for patient reporting