Missing data in longitudinal studies : strategies for Bayesian modeling and sensitivity analysis.

McClintock, P. V. E. (2010) Missing data in longitudinal studies : strategies for Bayesian modeling and sensitivity analysis. Contemporary Physics, 51 (2). pp. 188-189. ISSN 0010-7514

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Abstract

Longitudinal studies are almost always plagued by missing data. Examples include research data in public health, medicine, life and social sciences, as well as in environmental and geophysical studies of ice ages and the past climate. In the case of data involving people over a period of time there are usually some drop-outs due to e.g. moving away, or death, or simply disinclination to continue. Similarly, some physical measurements in a historic time series may be corrupted or missing. In such cases it may be impossible to repeat the measurements, or the survey, so that the best that can be done is to find a way of accommodating the lacunæ such that they do minimal damage to the reliability of any conclusions.

Item Type: Journal Article
Journal or Publication Title: Contemporary Physics
Additional Information: Review of book "Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis", by M.J. Daniels and J.W. Hogan.
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qc
Subjects:
Departments: Faculty of Science and Technology > Physics
ID Code: 35033
Deposited By: Professor P. V. E. McClintock
Deposited On: 05 Jan 2011 11:55
Refereed?: No
Published?: Published
Last Modified: 19 Sep 2019 23:50
URI: https://eprints.lancs.ac.uk/id/eprint/35033

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