A systematic review of models to predict recruitment to multicentre clinical trials
A systematic review of models to predict recruitment to multicentre clinical trials
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DOI：
10.1186/1471-2288-10-63
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发表时间：
2010-07-06
影响因子：
4
通讯作者：
Cook, Andrew
中科院分区：
文献类型：
--
作者：
Barnard, Katharine D.;Dent, Louise;Cook, Andrew
背景资料：不到三分之一的公共供资审判能够按照原计划征聘人员，这往往导致要求增加资金和（或）延长时间。其目的是确定模型，这可能是有用的随机对照试验的主要公共资助者时，估计可能的时间要求招募试验参与者。一个有用的模型的要求被确定为可用性，经验的基础上，能够反映时间的趋势，占中心招聘和贡献的委托decision.Methods：一个系统的审查英语文章使用MEDLINE和EMBASE。检索词包括：随机对照试验、患者、招募、预测、招募、模型、统计;贝叶斯定理;决策理论;蒙特卡罗方法和泊松。仅纳入了讨论使用建模方法预测试验招募的研究。从文章中提取的信息由一个作者，并检查了第二，使用预定义的form.Results：出326识别摘要，只有8个符合所有的纳入标准。在这8项研究中，主要讨论了5类模型：无条件模型、条件模型、泊松模型、贝叶斯模型和马尔可夫模型的蒙特卡罗模拟。所有这些满足所有预先确定的需求的资助者。结论：为了满足一些研究计划的需要，一个新的模式是需要作为一个重要的问题。任何选择的模型都应该根据回顾性和前瞻性数据进行验证，以确保它给出的预测上级目前使用的预测。
Background: Less than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision.Methods: A systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form.Results: Out of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder.Conclusions: To meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used.