Strategy for modelling non-random missing data mechanisms in observational studies using Bayesian methods

Mason, Alexina and Best, Nicky and Richardson, Sylvia and PLEWIS, IAN (2010) Strategy for modelling non-random missing data mechanisms in observational studies using Bayesian methods. Technical Report. Imperial College London. (Submitted)

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Abstract

Observational studies are notoriously full of non-responses and missing values. Bayesian full probability modelling provides a °exible approach for analysing such data, allowing a plausible model to be built which can then be adapted to carry out a range of sensitivity analyses. In this context, we propose a strategy for using Bayesian methods for a `statistically principled' investigation of data which contains missing covariates and missing responses, likely to be non- random. The ¯rst part of this strategy entails constructing a `base model' by selecting a model of interest, then adding a sub-model to impute the missing covariates followed by a sub-model to allow informative missingness in the response. The second part involves running a series of sensitivity analyses to check the robustness of the conclusions. We implement our strategy to investigate some typical research questions relating to the prediction of income, using data from the Millennium Cohort Study.

Item Type:Working Paper (Technical Report)
Subjects:3. Data Quality and Data Management > 3.6 Nonresponse
5. Quantitative Data Handling and Data Analysis > 5.7 Longitudinal Data Analysis
ID Code:1776
Deposited By: BIAS user
Deposited On:06 Apr 2011 14:26
Last Modified:06 Apr 2011 14:26

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