relation: http://eprints.ncrm.ac.uk/1776/
title: Strategy for modelling non-random missing data mechanisms  in observational studies using Bayesian methods
creator: Mason, Alexina 
creator: Best, Nicky
creator: Richardson, Sylvia
creator: PLEWIS, IAN
subject: 3.6 Nonresponse
subject: 5.7 Longitudinal Data Analysis
description: 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.
publisher: Imperial College London
date: 2010
type: Working Paper
type: NonPeerReviewed
format: application/pdf
language: en
identifier: http://eprints.ncrm.ac.uk/1776/1/StrategySubmitted.pdf
identifier:   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)