Real-World Occupational Epidemiology, Part 3: An Aggregate Data Analysis of Selikoff's “20-Year Rule”

Tran, Duy; Beh, Eric J.; Smith, Derek

doi:10.1080/10937404.2012.678766

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Therefore, in the case of EI, rather than trying to estimate the cell frequencies, an alternative strategy is to focus on the association structure between the variables. Analysing aggregate data using this strategy can be undertaken using the aggregate association index, or AAI, proposed by Beh (2008Beh ( , 2010 and discussed in terms of occupational epidemiology by Tran, Beh and Smith (2012). This index quantifies the extent of association that may exist between two dichotomous variables at the α level of significance, given only the aggregate data from a single two-by-two contingency table.…”

Section: Abstract: Aggregate Association Index Dual-system Estimatiomentioning

confidence: 99%

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

Brown¹,

Beh²,

Hudson³

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

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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.

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Section: Abstract: Aggregate Association Index Dual-system Estimatiomentioning

confidence: 99%

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

Brown¹,

Beh²,

Hudson³

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

View full text Add to dashboard Cite

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Power Transformations and Reciprocal Averaging

Beh,

Lombardo,

Wang

2023

Behaviormetrics: Quantitative Approaches to Human Behavior

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Profile transformations for reciprocal averaging and singular value decomposition

Wang,

Beh,

Lombardo

et al. 2024

Comput Stat

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Power transformations of count data, including cell frequencies of a contingency table, have been well understood for nearly 100 years, with much of the attention focused on the square root transformation. Over the past 15 years, this topic has been the focus of some new insights into areas of correspondence analysis where two forms of power transformation have been discussed. One type considers the impact of raising the joint proportions of the cell frequencies of a table to a known power while the other examines the power transformation of the relative distribution of the cell frequencies. While the foundations of the graphical features of correspondence analysis rest with the numerical algorithms like reciprocal averaging, and other analogous techniques, discussions of the role of power transformations in reciprocal averaging have not been described. Therefore, this paper examines this link where a power transformation is applied to the cell frequencies of a two-way contingency table. In doing so, we show that reciprocal averaging can be performed under such a transformation to obtain row and column scores that provide the maximum association between the variables and the greatest discrimination between the categories. Finally, we discuss the connection between performing reciprocal averaging and singular value decomposition under this type of power transformation. The function, is included in the Appendix and performs reciprocal averaging of a power transformation of the cell frequencies of a two-way contingency table.

show abstract

Real-World Occupational Epidemiology, Part 3: An Aggregate Data Analysis of Selikoff's “20-Year Rule”

Cited by 8 publications

References 19 publications

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

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

Power Transformations and Reciprocal Averaging

Profile transformations for reciprocal averaging and singular value decomposition

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