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In epidemiological studies, the capture-recapture (CRC) method is a powerfultool that can be used to estimate the number of diseased cases or potentiallydisease prevalence based on data from overlapping surveillance systems.Estimators derived from log-linear models are widely applied by epidemiologistswhen analyzing CRC data. The popularity of the log-linear model framework islargely associated with its accessibility and the fact that interaction termscan allow for certain types of dependency among data streams. In this work, weshed new light on significant pitfalls associated with the log-linear modelframework in the context of CRC using real data examples and simulationstudies. First, we demonstrate that the log-linear model paradigm is highlyexclusionary. That is, it can exclude, by design, many possible estimates thatare potentially consistent with the observed data. Second, we clarify the waysin which regularly used model selection metrics (e.g., information criteria)are fundamentally deceiving in the effort to select a best model in thissetting. By focusing attention on these important cautionary points and on thefundamental untestable dependency assumption made when fitting a log-linearmodel to CRC data, we hope to improve the quality of and transparencyassociated with subsequent surveillance-based CRC estimates of case counts.

On some pitfalls of the log-linear modeling framework for capture-recapture studies in disease surveillance