No / Real-world clinical setting is the major arena of traditional Chinese medicine (TCM) as it has experienced long-term practical clinical activities, and developed established theoretical knowledge and clinical solutions suitable for personalized treatment. Clinical phenotypes have been the most important features captured by TCM for diagnoses and treatment, which are diverse and dynamically changeable in real-world clinical settings. Together with clinical prescription with multiple herbal ingredients for treatment, TCM clinical activities embody immense valuable data with high dimensionalities for knowledge distilling and hypothesis generation. In China, with the curation of large-scale real-world clinical data from regular clinical activities, transforming the data to clinical insightful knowledge has increasingly been a hot topic in TCM field. This chapter introduces the application of data warehouse techniques and data mining approaches for utilizing real-world TCM clinical data, which is mainly from electronic medical records. The main framework of clinical data mining applications in TCM field is also introduced with emphasizing on related work in this field. The key points and issues to improve the research quality are discussed and future directions are proposed.

A method of designing early clinical trials is developed for finding an optimum dose level of a new drug to be recommended for use in later phases. During the trial, the efficacious doses are allocated to the patients more often and those with a high probability of toxicity are less likely to be chosen. The method proposed is adaptive in the sense that the statistical models are updated after the data from each cohort of patients are collected and the dose level is adjusted at each stage based on the current data. Two classes of designs are presented. Although both are for efficacy and toxicity responses, one of them also considers pharmacokinetic information. The dose optimisation criteria are based on the probability of success and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. They can be constrained by both acceptable levels of the probability of toxicity and desirable levels of the area under the concentration curve or the maximum concentration. The method presented is general and can be applied to various dose-response and pharmacokinetic models. To illustrate the methodology, it is applied to two different classes of models. In both cases, the pharmacokinetic model incorporates the population variability by making appropriate assumptions about the model parameters, while the dose responses are assumed to be either trinomial or bivariate binomial. Various design properties of the method are examined by simulation studies. Efficiency measures and the sensitivity of the designs to the assumed prior parameter values are presented. All of the computations are conducted in R, where the D- v optimal sampling time points are obtained by using the package PFIM. The results show that the proposed adaptive method works well and could be appropriate as a seamless phase IB/IIA trial design.

Thesis (Ph. D.)--University of Sydney, 2006. / Title from title screen (viewed 16 May 2007). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Physiotherapy, Faculty of Health Sciences. Includes bibliographical references. Also available in print form.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 203-220). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 203-220). Also available on the Internet.

Thesis (Ed. D.)--University of Virginia, 2005. / Includes bibliographical references (leaves 91-97).

Thesis (Ed. D.)--University of Virginia, 2005. / Includes bibliographical references (leaves 91-97). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.

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Trustworthy clinical practice guidelines assist health care professionals in selecting the management options that optimize patient health outcomes. The development of trustworthy guidelines requires the consideration of many aspects and the involvement of multiple contributors, often working in groups. The guideline panel plays the key role in the development process as it is responsible for prioritizing topics that should be covered as part of the guideline effort, formulating questions, reviewing the evidence, developing and agreeing on the recommendations, and endorsing the final guideline document. Ensuring transparency throughout the process by appropriately organizing and documenting panel activities is an essential standard that is used to assess the credibility of a developed guideline and its resulting recommendations. The adoption of conceptual frameworks that systematically guides panel members in their decision-making process (e.g. the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision (EtD) frameworks) can aid in the formulation of methodologically sound recommendations. In this dissertation, I used the example of a guideline on diagnosis and treatment of autism spectrum disorders to describe how rigorous research methods can support guideline panels in the development process from early stages to the formulation of recommendations. In another prominent guideline development effort with the American Society of Hematology, I have identified two steps in the process where panel members may benefit from further support and addressed these gaps by conceptualizing and developing novel approaches. The first approach comprises modelling baseline risk estimates for patient-important outcomes when only surrogate data is available. The second approach proposes a method to estimate decision thresholds for judgments on health benefits and harms using the GRADE EtD framework. While these approaches are tailored to address specific guideline panel needs, guideline methodologists could use the underlying concepts to find solutions to aid guideline panels in other steps of the development process. / Thesis / Doctor of Philosophy (PhD) / Clinical practice guidelines assist health care professionals in selecting management options that can best improve the health outcomes of their patients. The development of trustworthy guidelines is a complex process that requires the contribution of several entities. The guideline panel, which typically comprises different experts (clinicians, patient representatives, experts in research methodologies) plays the key role in this process as it is responsible for selecting the most important questions to address in the guideline, reviewing the evidence supporting an option, agreeing on the recommendations, and endorsing the final guideline document. To ensure that the process of developing guidelines is transparent and that the recommendations are credible, it is important that panel activities are well documented and follow rigorous methods. Structured frameworks, such as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision (EtD) approach, have been developed to systematically guide the panel members and to minimize the error that could be introduced while making decisions. In this thesis, I describe the development of an approach and its application for comprehensive guideline development by the Italian National Health Institute, to describe rigorous guideline development and propose two novel approaches to further assist panel members in enhancing their guideline development. The first of these two enhancements to guideline development describes how to derive a modelled estimate of the risk of having certain health conditions when this data is not directly available in the medical literature. The second of the two enhancements is a method to support guideline panels in judging how substantial the desirable and undesirable effects of health interventions are. Both approaches were tailored to fit specific needs but can be adapted to inform the improvement of other steps in the guideline development process.