Meta-analysis is now commonly used in medical research. However there are statistical issues relating to the subject that require investigation and some are considered here, from both a methodological and a practical perspective. Each of the fixed effect and the random effects models for meta-analysis are based on certain assumptions and the validity of these is investigated. A formal test of the homogeneity assumption made in the fixed effect model may be performed. Since the test has low power, simulation was used to investigate the power under various conditions. The random effects model incorporates a between-study component of variance into the model. A likelihood based method was used to obtain a confidence interval for this variance and also to provide an interval for the overall treatment effect which takes into account the fact that the between-study variance is estimated, rather than assuming it to be known. In order to obtain confidence intervals for the treatment effect for both the fixed effect and the random effects models, distributional assumptions of normality are usually made. Such assumptions may be checked using q-q plots of the residuals obtained for each trial in the meta-analysis. In both meta-analysis models it is assumed that the weight allocated to each study is known, when in fact it must be estimated from the data. The effect of estimating the weights on the overall treatment effect estimate, its confidence intervals, the between-study variance estimate and the test statistic for homogeneity, is investigated by both analytic and simulation methods. It is shown how meta-analysis methods may be used to analyse multicentre trials of a paired cluster randomised design. Meta-analysis techniques are found to be preferable to previously published methods specifically developed for the analysis of such designs, which produce biased and potentially misleading results when a large treatment effect is present.

Thesis (Ph. D.)--University of Toronto, 2004. / Includes bibliographical references.

Thesis (Ph.D.) - University of Glasgow, 2008. / Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.

Thesis (M.A.)--Georgia State University, 2008. / Title from file title page. Jeffrey Bennett, committee chair; Jaye Atkinson, Marian Meyers, committee members. Electronic text (118 p.) : digital, PDF file. Description based on contents viewed July 16, 2008. Includes bibliographical references (p. 109-118).

Thesis (M. Med. Sc.)--University of Hong Kong, 2004. / Also available in print.

published_or_final_version / Community Medicine / Master / Master of Public Health

The concept of collecting Adverse Events (AEs) arose with the advent of the Thalidomide incident. Prior to this the development and marketing of drugs was not regulated in any way. It was the teterogenic effects which raised people's awareness of the damage prescription drugs could cause. This thesis will begin by describing the background to the foundation of the Committee for the Safety of Medicines (CSM) and how AEs are collected today. This thesis will investigate survival analysis, discriminant analysis and logistic regression to identify prognostic indicators. These indicators will be developed to build, assess and compare predictor models produced to see if the factors identified are similar amongst the methodologies used and if so are the background assumptions valid in this case. ROC analysis will be used to classify the prognostic indices produced by a valid cut-off point, in many medical applications the emphasis is on creating the index - the cut-off points are chosen by clinical judgement. Here ROC analysis is used to give a statistical background to the decision. In addition neural networks will be investigated and compared to the other models. Two sets of data are explored within the thesis, firstly data from a Phase III clinical trial used to assess the efficacy and safety of a new drug used to repress the advance of Alzheimer's disease where AEs are collected routinely and secondly data from a drug monitoring system used by the Department of Rheumatology at the Haywood Hospital to identify patients likely to require a change in their medication based on their blood results.

No description available.

No description available.

No description available.