Can we use the approaches of ecological inference to learn about the potential for dependence bias in dual-system estimation: application to cancer registration data?

Brown, J, Beh, E and Hudson, I 2015, 'Can we use the approaches of ecological inference to learn about the potential for dependence bias in dual-system estimation: application to cancer registration data?', in Proceedings from the 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast, Australia, 29 November - 4 December 2015, pp. 9675-9681.


Document type: Conference Paper
Collection: Conference Papers

Title Can we use the approaches of ecological inference to learn about the potential for dependence bias in dual-system estimation: application to cancer registration data?
Author(s) Brown, J
Beh, E
Hudson, I
Year 2015
Conference name MODSIM2015
Conference location Gold Coast, Australia
Conference dates 29 November - 4 December 2015
Proceedings title Proceedings from the 21st International Congress on Modelling and Simulation (MODSIM2015)
Publisher The Modelling and Simulation Society of Australia and New Zealand
Place of publication Australia
Start page 9675
End page 9681
Total pages 7
Abstract The dual-system estimator, or estimators with a similar underlying set of assumptions and structure, is a widely used approach to estimate the unknown size of a population. Within official statistics its use is linked with population census, while in health applications it is often used to estimate true levels of incidence from imperfect reporting systems; the classic example being work by Sekar and Deming exploring the estimation of births in India in the 1940s. Critical to the implementation of dual-system estimation are the assumptions that the probability of being counted in a source is homogeneous and that the event of being counted in each source is independent. When either of these assumptions fails, the two by two table will have an odds ratio different to one and the dual-system estimator will be biased. Inferential frameworks such as the aggregate association index (AAI) have been developed to allow the researcher to assess the plausibility of independence between two variables in a two by two table, when only the margins are observed. Given any appropriate measure of relationship, this strategy relies on determining the AAI, which provides an indication of the likely association structure between the variables given only the marginal information. Further advances of the AAI have also been established including its link with the odds ratio and its relationship with the size of the study being undertaken. Determining the population size from a two by two table given limited information is an alternative variation of the framework on which the AAI is built. Therefore the underlying theoretical properties of the two by two table are identical in both scenarios - it is only the nature of the unknown information that differs. In this paper we make the first steps to exploring the use of an AAI type framework (and its relatives) to assess the plausibility of an independence assumption in applications of population size estimation. We use alternative data set-ups
Subjects Statistics not elsewhere classified
Keyword(s) aggregate association index
dual-system estimation
dependence
ecological inference
measures of association
ISBN 9780987214355
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