论文标题
将动态线性模型应用于协变量的随机分配临床试验
Application of Dynamic Linear Models to Random Allocation Clinical Trials with Covariates
论文作者
论文摘要
Lee，Boone等人（2020）提出了一种使用动态线性模型来改善随机分配模型中首选治疗分配预算的方法。但是，该模型未能包括吸烟，性别等对模型性能的影响协变量。当前的论文解决了使用单个协变量的贝叶斯自适应分配模型中DLM的随机分配。我们显示了减少的治疗分配预算，以及时间减少以找到首选治疗。此外，对均值和方差参数进行灵敏度分析，并使用贝叶斯因子进行功率分析。该功率分析用于确定未分配的患者预算比指定截止值的比例。另外，对协变量系数进行了灵敏度分析。
A recent method using Dynamic Linear Models to improve preferred treatment allocation budget in random allocation models was proposed by Lee, Boone, et al (2020). However this model failed to include the impact covariates such as smoking, gender, etc, had on model performance. The current paper addresses random allocation to treatments using the DLM in Bayesian Adaptive Allocation Models with a single covariate. We show a reduced treatment allocation budget along with a reduced time to locate preferred treatment. Furthermore, a sensitivity analysis is performed on mean and variance parameters and a power analysis is conducted using Bayes Factor. This power analysis is used to determine the proportion of unallocated patient budgets above a specified cutoff value. Additionally a sensitivity analysis is conducted on covariate coefficients.
