No description available.

No description available.

No description available.

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.

This thesis aimed to improve the methodology of disease-modification clinical trials in neurodegenerative disorders, with particular reference to Parkinson's disease (PD) and Alzheimer's disease. A systematic review was undertaken to determine what biomarkers for disease progression in PD exist, and whether any have sufficient evidence to be used in clinical trials. Included studies (n=183) were generally of poor quality, being cross-sectional with small numbers of participants, applying excessive inclusion/exclusion criteria, having flawed methodologies and applying simplistic statistical analyses. Insufficient evidence was, therefore, found to recommend the use of any disease progression biomarker in PD clinical trials. A subsequent review in Alzheimer's disease (n=59) demonstrated that these issues were not unique to PD. A 'roadmap' was, therefore, developed to improve future disease progression biomarker studies. The sensitivity to change of a range of PD clinical outcome measures was analysed using data from a follow-up study of an incident cohort of patients with parkinsonism. The MMSE, total UPDRS and PDQ-39 summary index were the most sensitive to change of the continuous outcome measures examined. Amongst binary outcome measures, a new 'dead or dependent' outcome measure was most sensitive to change, and was shown to be a feasible outcome measure for future PD RCTs. Finally, a systematic review was undertaken to examine the validity of differing clinical trial designs used in Alzheimer's disease and PD to demonstrate disease-modification. A variety of design strategies, including wash-in and wash-out analyses, delayed-start designs and long-term follow-up studies, have been used but have methodological limitations. No evidence was found of novel clinical trial designs having been used previously or planned for use in the future. Final recommendations are made that future disease-modification trials should be long-term follow-up studies involving newly diagnosed patients. 'Dead or 'dependent' is highlighted as an efficacious measure to use in such trials.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xiii, 241 p.; also includes graphics. Includes abstract and vita. Advisor: Heaney, Catherine, School of Public Health. Includes bibliographical references (p. 186-201).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 61-68).

One of the most important questions in the planning of medical experiments to assess the performance of new drugs or treatments, is how big to make the trial. The problem, in its statistical formulation, is to determine the optimal size of a trial. The most frequently used methods of determining sample size in clinical trials is based on the required p-value, and the required power of the trial for a specified treatment effect. In contrast to the Bayesian decision theoretic approach there is no explicit balancing of the cost of a possible increase in the size of the trial against the benefit of the more accurate information which it would give. In this work we consider a fully Bayesian (or decision theoretic) approach to sample size determination in which the number of subsequent users of the therapy under investigation, and hence also the total benefit resulting from the trial, depend on the strength of the evidence provided by the trial. Our procedure differs from the usual Bayesian decision theory methodology, which assumes a single decision maker, by recognizing the existence of three decision makers, namely: the pharmaceutical company conducting the trial, which decides on its size; the regulator, whose approval is necessary for the drug to be licenced for sale; and the public at large, who determine the ultimate usage. Moreover, we model the subsequent usage by plausible assumptions for actual behaviour, rather than assuming that this represents decisions which are in some sense optimal. For this reason the procedure may be called "Behavioural Bayes" (or BeBay for short), the word Bayes referring to the optimization of the sample size. In the BeBay methodology the total expected benefit from carrying out the trial minus the cost of the trial is maximized. For any additional sales to occur as a result of the trial it must provide sufficient evidence both to convince the regulator to issue the necessary licence and to convince potential users that they should use the new treatment. The necessary evidence is in the form of a high probability after the trial that the new treatment achieves a clinically relevant improvement compared to the alternative treatment. The regulator is assumed to start from a more sceptical and less well-informed view of the likely performance of the treatment than the company carrying out the trial. The total benefit from a conclusively favourable trial is assessed on the basis of the size of the potential market and aggregated over the anticipated life-time of the product, using appropriate discounting for future years.

Dissertation (Ph.D.) -- Worcester Polytechnic Institute. / Keywords: Alometric scaling; Radiopharmaceuticals. Includes bibliographical references (p.158-163).

Thesis (M. P. H.)--University of Hong Kong, 2007.