Optimal checking procedures for monitoring laboratory analyses

Many clinical, environmental, and epidemiologic studies rely heavily upon biochemical data, and the quality of these data is of paramount importance to the validity of study conclusions. Traditionally, far more attention has been given to the analysis of study data than has been given to monitoring the quality of the data. In this paper we draw an analogy between monitoring a laboratory system and an industrial production process and discuss the limitations of industrial quality control plans when applied in a laboratory setting. We derive methods for computing optimal checking schedules for laboratory analyses. These schedules formalize traditional laboratory practices of periodic checking and provide guidelines for the frequency and placement of checks within a finite batch of analyses. When laboratory system failure can be reasonably approximated by an exponential or geometric distribution, optimal checking schedules are relatively easy to compute. For more complex failure distributions, we present a dynamic programming approach. We describe an application to the measurement of selenium status in plasma samples using an electrothermal atomic absorption spectrophotometry procedure.