Pharmaceutical R&D is time-consuming, extremely costly and involves great uncertainty. Although there is a broad range of literature on statistical issues in clinical trials, there is not much that focuses directly on the modelling of pre-clinical research. This thesis investigates models and associated software for improving decisionmaking in this area, building on earlier work by the same research group. We introduce a class of adaptive policies called forwards induction policies for candidate drug selection, and show that these are optimal, with a straightforward solution algorithm, within a restricted setting, and are usually close to optimal more generally. We also introduce an adaptive probabilities model that allows the incorporation of learning from a project’s progress into the planning process. Real options analysis in the evaluation of project value is discussed. Specifically, we consider the option value of investing in clinical trials once a candidate drug emerges from pre-clinical research. Simulation algorithms are developed to investigate the probability distributions of the total reward, total cost, profitability index and the required future resource allocations of a pharmaceutical project under a given allocation plan. The ability to simulate outcome distributionsmeans that we can also compare the riskiness of different projects and portfolios of projects.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:568067 |
| Date | January 2011 |
| Creators | Qu, Shuo |
| Contributors | Gittins, John |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:1a73a652-9e85-4952-b6ef-8aeb83917cdf |
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