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dc:title "HTML Summary of #1776 \n\nStrategy for modelling non-random missing data mechanisms
\nin observational studies using Bayesian methods\n\n";
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bibo:abstract "Observational studies are notoriously full of non-responses and missing values. Bayesian full\r\nprobability modelling provides a °exible approach for analysing such data, allowing a plausible\r\nmodel to be built which can then be adapted to carry out a range of sensitivity analyses. In\r\nthis context, we propose a strategy for using Bayesian methods for a `statistically principled'\r\ninvestigation of data which contains missing covariates and missing responses, likely to be non-\r\nrandom.\r\nThe ¯rst part of this strategy entails constructing a `base model' by selecting a model of\r\ninterest, then adding a sub-model to impute the missing covariates followed by a sub-model\r\nto allow informative missingness in the response. The second part involves running a series of\r\nsensitivity analyses to check the robustness of the conclusions. We implement our strategy to\r\ninvestigate some typical research questions relating to the prediction of income, using data from\r\nthe Millennium Cohort Study."^^xsd:string;
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dct:date "2010";
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skos:prefLabel "3.6 Nonresponse"@en .
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skos:prefLabel "5.7 Longitudinal Data Analysis"@en .
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