Validity and efficiency issues are considered with regard to the use of matching and random sampling as alternative methods of subject selection in follow-up and case-control studies. We discuss the simple situation involving dichotomous disease and exposure variables and a single dichotomous matching factor, and we consider the influence on efficiency of a possible loss of subjects due to matching constraints. The decision to match or not should be motivated by efficiency considerations. An efficiency criterion based on a comparison of confidence intervals under matching and random sampling for the effect measure of interest (the risk ratio and risk difference in follow-up studies, and the odds ratio in case-control studies) leads to the following conclusions when the sampling method does not influence the size of the comparison group. In follow-up studies, matching on a confounder is expected to lead to a gain in efficiency over random sampling, while matching on a nonconfounder is not expected to result in a loss of efficiency. In case-control studies, the same conclusions hold, except that matching is not as advantageous as in follow-up studies and can lead to a loss of efficiency in some situations (usually of little practical importance). When matching reduces the size of the comparison group, there is likely to be a meaningful gain in efficiency due to random sampling only when the matched comparison group is at most 40-50% the size of the randomly-sampled comparison group is a follow-up study, and at most 50-65% the size in a case-control study.

Applying control charts successfully to batch processes requires the recognition of some distinguishing features of batch processing versus single‐unit processing. Traditional Shewhart‐type charts can be modified for use in batch‐processing practitioners of statistical process control (SPC); however, entirely different control chart methods and practices will often be required. This article reviews the common features of batch processing and the data available to control chart practitioners from typical batch processes and suggests alternatives to standard Shewhart charts for effective process monitoring and control.

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