Akacha, Mouna (2011) Analysis of repeated measurements with missing data. PhD thesis, University of Warwick.

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Abstract

This thesis discusses issues arising in the analysis of repeated measurement studies with
missing data.
The first part of the thesis is motivated by a study where continuous and bounded longitudinal
data form the outcome of interest. The aim of this study is to investigate the change
over time in the outcome variable and factors that influence this change. The analysis is
complicated because some patients withdraw from the study, leading to an incomplete data
set.
We propose a non-linear mixed model that specifies the rate of change and the
bounds of the outcome as a function of covariates. This mixed model has advantages over
transforming the data and is easy to interpret. We discuss different models for the covariance
structure of bounded continuous longitudinal data.
To explore the impact of missingness, we perform several sensitivity analyses. Further,
we propose a model for informative missingness, taking into account the number and
nature of reminders made to contact initial non-responders, and evaluate the impact of missingness
on estimates of change. We contrast this model with the traditional selection model,
where the missingness process is modelled.
Our investigations suggest that using the richer information of the reminder process
enables a more accurate choice of covariates which induce missingness, than modelling the
missingness process. Regarding the reminder process, we observe that phone calls are most
effective.
The second part of this thesis is motivated by dose-finding studies, where the number of
events per subject within a specified study period form the primary outcome. These studies
aim to identify a target dose for which the new drug can be shown to be as effective as a
competitor medication. Given a pain-related outcome, we expect many patients to drop out
before the end of the study. The impact of missingness on the analysis and models for the
missingness process must be carefully considered.
The recurrent events are modelled as over-dispersed Poisson process data, with dose
as regressor. Additional covariates may be included. Constant and time-varying rate functions
are examined. Based on a range of such models, the impact of missingness on the
precision of the target dose estimation is evaluated by simulations. Five different analysis
methods are assessed: a complete case analysis; two analyses using different single imputation techniques; a direct likelihood analysis; and an analysis using pattern-mixture models.
The target dose estimation is robust if the same missingness process holds for the
target dose group and the active control group. This robustness is lost as soon as the missingness
mechanisms for the active control and the target dose differ. Of the methods explored,
the direct-likelihood approach performs best, even when a missing not at random
mechanism holds.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics R Medicine > R Medicine (General)
Library of Congress Subject Headings (LCSH):	Medical statistics, Missing observations (Statistics)
Official Date:	March 2011
Dates:	Date Event March 2011 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hutton, Jane L.
Sponsors:	University of Warwick. Centre for Research in Statistical Methodology ; International Biometric Society
Extent:	xix, 206 leaves : charts
Language:	eng

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