Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs through extensive simulation studies using a recently published trial as motivation. For simplicity, a two-armed trial with binary endpoints and immediate responses is considered. Simulation results for the proposed design show that: (i) the percentage of patients allocated to the superior arm is much higher than in the traditional fixed randomised design; (ii) relative to the optimal DP design, the power is largely improved upon and (iii) it exhibits only a very small bias and mean squared error of the treatment effect estimator. Furthermore, this design is fully randomised which is an advantage from a practical point of view because it protects the trial against various sources of bias. As such, the proposed design addresses some of the key issues that have been suggested as preventing so-called bandit models from being implemented in clinical practice.
We analyze a comprehensive model for multi-class job scheduling accounting for user abandonment, with the objective of minimizing the total discounted or time-average sum of linear holding costs and abandonment penalties. We assume geometric service times and Bernoulli abandonment probabilities. We solve analytically the case in which there are 1 or 2 users in the system to obtain an optimal index rule. For the case with more users we use recent advances from the restless bandits literature to obtain a new simple index rule, denoted by AJN, which we propose to use also in the system with arrivals. In the problem without abandonment, the proposed rule recovers the cµ-rule which is well-known to be optimal both without and with arrivals. Under certain conditions, our rule is equivalent to the cµ/θ-rule, which was recently proposed and shown to be asymptotically optimal in a multi-server system with overload conditions. We present results of an extensive computational study that suggest that our rule is almost always superior or equivalent to other rules proposed in the literature, and is often optimal.
In this paper we address the problem of fast and fair transmission of flows in a router, which is a fundamental issue in networks like the Internet. We model the interaction between a source using the Transmission Control Protocol (TCP) and a bottleneck router with the objective of designing optimal packet admission controls in the router queue. We focus on the relaxed version of the problem obtained by relaxing the fixed buffer capacity constraint that must be satisfied at all time epoch. The relaxation allows us to reduce the multi-flow problem into a family of single-flow problems, for which we can analyze both theoretically and numerically the existence of optimal control policies of special structure. In particular, we show that for a variety of parameters, TCP flows can be optimally controlled in routers by so-called index policies, but not always by threshold policies. We have also implemented the index policy in Network Simulator-3 and tested in a simple topology their applicability in real networks. The simulation results show that the index policy achieves a wide range of desirable properties with respect to fairness between different TCP versions, across users with different round-trip-time and minimum buffer required to achieve full utility of the queue.
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