## HCSL Publications

# Statistical Quality Control, Reliability, and Risk

### 1.
Hatjimihail AT. Estimation of the optimal statistical quality control sampling
time intervals using a residual risk measure. PLoS ONE 2009 4(6): e5770.
doi:10.1371/journal.pone.0005770

#### Abstract

Background: An open problem in clinical chemistry is the estimation of the optimal sampling time intervals for the application of statistical quality control (QC) procedures that are based
on the measurement of control materials. This is a probabilistic risk assessment problem that
requires reliability analysis of the analytical system, and
the estimation of the risk caused by the measurement error.

Methodology/Principal Findings: Assuming that the states of the
analytical system are the reliability state, the maintenance state, the
critical-failure modes and their combinations,
we can define risk functions based on the mean time of the states, their
measurement error and the medically acceptable measurement error. Consequently,
a residual risk measure rr
can be defined for each sampling time interval. The rr
depends on the state probability vectors of the analytical system, the state
transition probability matrices before and after each application of the QC
procedure and the state mean time matrices. As optimal sampling time intervals
can be defined those minimizing a QC related cost measure while the
rr is acceptable. I developed an algorithm that
estimates the rr for any QC sampling time interval of a
QC procedure applied to analytical systems with an arbitrary number of
critical-failure modes, assuming any failure time and measurement error
probability density function for
each mode. Furthermore, given the acceptable rr, it can
estimate the optimal QC sampling time intervals.

Conclusions/Significance: It is possible to rationally estimate the
optimal QC sampling time intervals of an analytical system to sustain an
acceptable residual risk with the minimum QC related cost. For the optimization
the reliability analysis of the analytical system and the risk analysis of the
measurement error are needed.

#### Comment

This publication presents a theoretical framework and a symbolic computation algorithm for the optimization of the statistical quality control of an analytical process, based on the reliability of the analytical system and the risk of the analytical error.

Abstract in
PubMed

Full Text in PLoS ONE

### 2.
Hatjimihail AT. Core laboratory QC [Letter to the editor]. CAP Today
2015;29(5):8.

Full text (PDF format)