Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719077
Title: Analysis of repeated measurements from medical research when observations are missing
Author: Walker, K.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2007
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Abstract:
Subject dropout is a common problem in repeated measurements health stud ies. Where dropout is related to the response, the results obtained can be substantially biased. The research in this thesis is motivated by a repeated measurements asthma clinical trial with substantial patient dropout. In practice the extent to which missing observations affect parameter esti mates and their efficiency is not clear. Through extensive simulation studies under various scenarios and missing data mechanisms, the effect on para meter estimates of missing observations is explored and compared. Bias in the model estimates is found to be sensitive to the missing data mechanism, the type of model used, the estimation method, and the type of response variable, amongst other factors. Findings from the simulation study highlight the importance of considering the likely dropout mechanism in choosing a model for the analysis of incom plete repeated measurements. For example, generalised estimating equations (GEE) require a missing completely at random (MCAR) assumption in gen eral, as does the summary statistics method. Several formal tests of MCAR have been published, and these tests are compared both quantitatively, and in terms of their various merits and limitations. Other than the sensitivity analysis, there are no widely accepted methods for analysing data with missing observations missing not at random (MNAR), as strong assumptions are required about the missing data mechanism. A method for incorporating cause of dropout into the analysis is proposed for MNAR data. A Bayesian hierarchical model is developed with informative priors for the bias of dropouts compared to completers for each cause of dropout. The feasibility of the proposed prior elicitation is investigated by consultation with clinicians. And the model is assessed through simulation studies, in which the sensitivity of the approach to misspecification of the parameters of the dropout mechanism is examined.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.719077  DOI: Not available
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