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Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

机译:自适应设计评估临床试验后的治疗效果

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Parameter estimation following an adaptive design or group sequential design has been extremely challenging due to potential random high from its face value estimate. In this paper, we introduce a new framework to model clinical trial data flow based on a marked point process (MPP). The MPP model allows us to use methods of stochastic calculus for analyses of any adaptive clinical trial. As an example, we apply this method to a two stage treatment selection design and derive a procedure to estimate the treatment effect. Numerical examples will be used to evaluate the performance of the proposed procedure.View full textDownload full textKey WordsAdaptive design, Clinical trial, Parameter estimation, Treatment selectionRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10543406.2012.676534
机译:自适应设计或群组顺序设计之后的参数估计非常具有挑战性,因为从其面值估计中可能存在随机高。在本文中,我们介绍了一种基于标记点过程(MPP)建模临床试验数据流的新框架。 MPP模型允许我们使用随机演算方法来分析任何适应性临床试验。例如,我们将此方法应用于两阶段治疗选择设计,并推导出评估治疗效果的程序。数值示例将用于评估所建议程序的性能。 twitter,technorati,美味,linkedin,facebook,stumbleupon,digg,google,更多“,发布:” ra-4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10543406.2012.676534

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