| Summary: | Personalized medicine is a rapidly growing area of research which has attracted much attention in recent years in the field of medicine. The ultimate aim of this approach is to ensure that the most appropriate treatment which provides clinical benefit will be tailored to each patient according their personal characteristics. However, testing the effectiveness of a biomarker-guided approach to treatment in improving patient health yields challenges both in terms of trial design and analysis. Although a variety of biomarker-guided designs have been proposed recently, their statistical validity, application and interpretation has not yet been fully explored. A comprehensive literature review based on an in-depth search strategy has been conducted with a view to providing researchers with clarity in definition, methodology and terminology of the various reported biomarker-guided trial designs. Additionally, a user-friendly online tool (www.BiGTeD.org) informed by our review has been developed to help investigators embarking on such trials decide on the most appropriate design. Simulation studies for the investigation of key statistical aspects of such trial designs and statistical approaches such as the sample size requirement under different settings have been performed. Furthermore, a strategy has been applied to choose the most optimal design in a given setting where a previously proposed clinical trial proved inefficient due to the very large sample size that was required. Statistical techniques to calculate the corresponding sample size have been applied and an adaptive version of the proposed design has been explored through simulations. Practical challenges of biomarker-guided trials in terms of funding, ethical and regulatory issues, recruitment, monitoring, statistical analysis plan, biomarker assessment and data sharing issues are also addressed in this thesis. The different biomarker-guided designs proposed so far need to be better understood by the research community in terms of analysis and planning and practical application as their proper use and choice can increase the probability of success of clinical trials which will result in development of personalised treatments in the future. Therefore, with this PhD thesis, we contribute to the knowledge enhancement of researchers regarding these studies by providing essential information and presenting statistical issues arising in their implementation. We hope that this work will help scientists to choose the right clinical trial design in the era of personalized medicine which is of utmost importance for the translation of drug development into the improvement of human health.
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