🤖 AI Summary
This study addresses multi-source list data featuring absorbing lists—such as death registries—and asymmetric interactions among sources. The authors propose a novel framework based on continuous-time Markov chains that, for the first time, integrates both absorption mechanisms and directional interactions into multi-list capture–recapture models. By explicitly modeling absorbing states and employing a log-linear structure, the method effectively mitigates estimation bias inherent in conventional approaches that neglect absorption. Empirical analyses on stroke epidemiology data and London drug-use records demonstrate that the proposed framework yields unbiased and robust estimates of total population size, substantially extending the applicability of existing capture–recapture methodologies.
📝 Abstract
We introduce a continuous-time Markov chain framework for estimating population size from multi-list data, which allows directional interactions to be modelled and can accommodate absorbing lists, such as death records, or more general data collection processes. The standard model of the continuous-time Markov chain framework and the log-linear model for multi-list data are equivalent when lists are independent and we show empirically that they give similar results in the presence of dependencies between lists. Through a simulation study, we highlight the need to account for an absorbing list by using the Markov model or the log-linear model with forced absorbing interactions, observing biased estimates of the population size otherwise. We motivate our approach with an epidemiological dataset concerning individuals suffering from a first ever stroke in North-West England, in which one of the lists is a death record. We illustrate a further use of our approach by considering a case of ordered lists on drug use data from the City of London.