Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. When attempting to determine how to respond optimally to a large-scale emergency, the ability to predict the consequences of certain courses of action in silico is of great utility. Agent-based simulations (ABSs) have become the de facto tool for this purpose, however they may be used and implemented in a variety of ways. This paper reviews existing implementations of ABSs for largescale emergency response, and presents a taxonomy classifying them by usage. Opportunities for improving ABS for large-scale emergency response are identified.

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a b s t r a c tDuring a major incident, the emergency services work together to ensure that those casualties who are critically injured are identified and transported to an appropriate hospital as fast as possible. If the incident is multi-site and resources are limited, the efficiency of this process is compromised as the finite resources must be shared among the multiple sites. In this paper, agent-based simulation is used to determine the allocation of resources for a two-site incident which minimizes the latest hospital arrival times for critically injured casualties. Further, how the optimal resource allocation depends on the distribution of casualties across the two sites is investigated. Such application supports the use of agentbased simulation as a tool to aid emergency response.

Early phase trials of complex interventions currently focus on assessing the feasibility of a confirmatory RCT and on conducting pilot work. These trials are not designed to enable a formal assessment of potential efficacy. As a result, guidance recommends any statistical analysis of treatment effects conducted in these trials should be treated with extreme caution and not used in deciding if a confirmatory trial of the intervention is warranted. Phase II trial designs developed in the drug context offer a potential solution, providing methods for selecting sample size parameters for feasibility and pilot trials which will enable formal assessments of potential efficacy to be carried out. In this paper we will outline the challenges encountered in extending ideas developed in the phase II drug trial literature to the complex intervention setting. The prevalence of multiple endpoints and clustering of outcome data will be identified as important considerations, having implications for timely and robust determination of optimal sample size parameters. The potential for Bayesian methods to help to identify robust trial designs and optimal decision rules will also be explored.

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of sample size determination problems, often minimising a single parameter (the overall sample size) subject to power being above a target level. We describe a general framework for solving simulation-based sample size determination problems with several design parameters over which to optimise and several conflicting criteria to be minimised. The method is based on an established global optimisation algorithm widely used in the design and analysis of computer experiments, using a non-parametric regression model as an approximation of the true underlying power function. The method is flexible, can be used for almost any problem for which power can be estimated using simulation, and can be implemented using existing statistical software packages. We illustrate its application to a sample size determination problem involving complex clustering structures, two primary endpoints and small sample considerations.