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The authors used "internal validity analysis" to evaluate the performance of various capture-recapture methods. Data from studies with five overlapping, incomplete lists generated subgroups whose known sizes were compared with estimates derived from various four-source capture-recapture analyses. In 15 data sets unanalyzed previously (five subgroups of each of three new studies), the authors observed a trend toward mean underestimation of the known population size by 16-25%. (Coverage of the 90% confidence intervals associated with the method found to be optimal was acceptable (13/15), despite the downward bias.) The authors conjectured that (with the obvious exception of geographically disparate lists) most data sets used by epidemiologists tend to have a net positive dependence; that is, cases captured by one source are more likely to be captured by some other available source than are cases selected randomly from the population, and this trend results in a bias toward underestimation. Attempts to ensure that the underlying assumptions of the methods are met, such as minimizing (or adjusting adequately) for the possibility of loss due to death or migration, as was undertaken in one exceptional study, appear likely to improve the behavior of these methods.

Accuracy of Alternative Approaches to Capture-Recapture Estimates of Disease Frequency: Internal Validity Analysis of Data from Five Sources