Detecting failed WBC-reduction processes: computer simulations of intermittent and continuous process failure

By regulation, ongoing process control of WBC-reduced processes is performed on 1 percent of WBC-reduced components, typically four to five samples per month. However, prospective study of the power of this small sample has been difficult. Using computer-generated "residual WBC" distributions, sample size sensitivity to continuous or intermittent WBC-reduction failure was examined.

Populations of log-normally distributed values (mean +/- SD, 4.5+/-0.5; n = 10(5)) were generated. Continuous failure (log-normality maintained) was simulated by incrementally increasing the population mean or its SD. Intermittent failure (bimodal distributions with discrete subpopulations of WBCs > the FDA cutoff) was simulated by admixing increasing percentages of secondary outlier populations. Sample sizes of 4 to 60 were examined (500 repetitions each) for their power to detect drift or failure by standard control criteria.

Normally distributed low variance failure was easily detected by comparison of the mean of four samples to an upper control limit (95% confidence of detecting 2% failure). However, 40 samples were required to detect > 5 percent intermittent (bimodal) failure or high variance failure with 90-percent confidence, and only if individual WBC values were compared to cutoff.

Sampling error limits the detection of high variance or bimodal distributions. While the mean of a small sample is highly sensitive to shifts in a low-variance normal distribution, the detection of a high-variance bimodal population requires a large number of individual values compared to cutoff. Therefore, the number of samples required for confident failure detection depends on both the nature of the underlying distribution and the interpretive criteria. Further research is necessary to determine the true distributions of WBC-reduction process failure, as well as clinically relevant quality limits.