临床试验优化：用于规划临床试验招募的蒙特卡洛模拟马尔可夫模型
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment.
作者信息
Abbas Ismail, Rovira Joan, Casanovas Josep
机构信息
Universitat Politècnica de Catalunya, Facultat d'Informàtica de Barcelona, Laboratori de Càlcul, Barcelona, Spain.
出版信息
Contemp Clin Trials. 2007 May;28(3):220-31. doi: 10.1016/j.cct.2006.08.002. Epub 2006 Aug 10.
INTRODUCTION
The patient recruitment process of clinical trials is an essential element which needs to be designed properly.
METHODS
In this paper we describe different simulation models under continuous and discrete time assumptions for the design of recruitment in clinical trials.
RESULTS
The results of hypothetical examples of clinical trial recruitments are presented. The recruitment time is calculated and the number of recruited patients is quantified for a given time and probability of recruitment. The expected delay and the effective recruitment durations are estimated using both continuous and discrete time modeling.
CONCLUSION
The proposed type of Monte Carlo simulation Markov models will enable optimization of the recruitment process and the estimation and the calibration of its parameters to aid the proposed clinical trials. A continuous time simulation may minimize the duration of the recruitment and, consequently, the total duration of the trial.
引言
临床试验的患者招募过程是一个需要妥善设计的关键要素。
方法
在本文中，我们描述了在连续时间和离散时间假设下用于临床试验招募设计的不同模拟模型。
结果
给出了临床试验招募的假设示例结果。计算了招募时间，并针对给定时间和招募概率对招募患者数量进行了量化。使用连续时间和离散时间建模估计了预期延迟和有效招募持续时间。
结论
所提出的蒙特卡洛模拟马尔可夫模型类型将能够优化招募过程，并对其参数进行估计和校准，以辅助所提议的临床试验。连续时间模拟可能会使招募持续时间最短，从而使试验的总持续时间最短。
Zotero 插件