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

Research output: Contribution to journalBook/Film/Article review


<mark>Journal publication date</mark>03/2010
<mark>Journal</mark>Contemporary Physics
Number of pages2
<mark>Original language</mark>English


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.

Bibliographic note

Review of book "Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis", by M.J. Daniels and J.W. Hogan.