摘要
适应性设计(adaptive design)是利用试验中陆续得到的数据(也可以包括外部数据),在不破坏试验的真实性和完整性的前提下,有计划地对后续试验做出调整的临床研究设计。适应性设计允许研究者在试验的实施过程中对试验进行调整,以达到提高试验成功率、降低研发成本的目的。在临床试验中涉及到的调整是多方面的,可能包括纳入和排除标准、样本含量、试验终点、诊断标准、统计分析方法、剂量、增加或删除试验组等。本文主要研究适应性设计中的样本量调整方法。
适应性设计中的样本量调整方法允许在试验开始后,利用手头现有数据信息,调整后续试验的样本含量,弥补试验初期参数设计的不确定性,以达到增加临床试验灵活性、提高试验成功率、减少不必要的受试者暴露于低效、无效甚至危险的治疗组中的目的。本研究系统地介绍了临床试验中的两类样本量调整方法:内部预试验和适应性成组序贯设计,并采用计算机模拟技术,评价盲态估计和揭盲估计的统计学性质;评价适应性成组序贯设计中CHW法、CRP法和ACR法在平均样本含量、I型错误控制和检验效能等方面的特点。
主要研究内容如下:
1.在采用内部预试验的临床试验中,在不同的初始方差估计值σ02、内部预试验样本比例(π)以及不同的统计分析方法下,评价采用盲态估计方法和揭盲估计方法的统计学性质。
2.评价适应性成组序贯设计中CHW法、CRP法和ACR法三种样本量调整方法的统计学性质,并对其应用策略作进一步的探讨。本研究的主要结论如下:
1.采用内部预试验的临床试验中,试验设计阶段,应充分了解药物的疗效信息,尽量避免试验初期设定的σ02过分小于真实方差σ2;建议在试验完成一半时进行方差的重估;采用校正t检验优于传统中心t检验和随机化检验,特别是当2σ0和/或π较小时;在实际临床试验中,尽可能采用盲法估计。
2.在适应性成组序贯设计的临床试验中,若采用CHW法,基于处理效应比的样本量调整方法和基于条件效能的样本量调整方法,在不同参数组合下,平均样本含量差异较大,前者得到的新样本含量和检验效能高于后者,两者的I型错误基本一致。在二阶段适应性成组序贯设计中,基于条件效能的CHW法与CRP法、ACR法在控制I型错误和样本含量调整方面基本一致;采用ACR法时,若第一次期中分析的条件效能介于0.3到0.8之间时,可以不对末次分析的终止界值作出调整,模拟试验证明,此时可以更好地控制I型错误。
The PhRMA working group defines an adaptive design as a clinical study design that uses accumulating data to decide on how to modify aspects of the study as it continues, without undermining the validity and integrity of the trial. Adaptive design is a dynamic process and allows modifications to the trial in order to improve the success rates and reduce the development costs. Depending upon the types of modification or adaption made, common employed data dependent changes to clinical trials may include, but are not limited to: modifications in inclusion/exclusion criteria, sample size adjustment, changes in hypotheses and/or study endpoints, changes to dosing schedule and duration of treatment, drop the inferior treatment group. In this paper, we focus on the study of methods about adaptive sample size adjustment.
Sample size re-estimation methods make it possible to adjust the final sample size according to the information from interim analysis after clinical trials set out. The uncertainty about the parameters at the design stage can be perfected by these methods for the purpose of making the design flexible and minimize the number of patients exposed to inferior or highly toxic treatment groups. In this research, the authors first summarize the techniques of sample size adjustment methods which can be fall into two main groups: internal pilot study and adaptive group sequential design. Simulation studies are then employed to evaluate these methods that mainly include:
1.Clinical trials with internal pilot study can be split into two types: blinded sample size re-estimation and unblinded sample size re-estimation. We evaluate the statistical characters of blinded and unblended sample size re-estimation methods under different initial variances, internal pilot sample fractions and statistical analysis procedures.
2.Evaluate the statistical characters of CHW method, CRP method and ACR method in adaptive group sequential design, and the application tactics are further discussed here.
The main results of this research are as follows:
1.If internal pilot study is employed in clinical trials, in the design stage, information about the effect size and variance should be well researched and avoid the situation that the initially estimated variance is much less than the true one; we suggest that the sample size should be adjusted after half of the planned patients have been enrolled; correction t-test is better than classical central t-test and permutation test (especially if the initially estimated variance and/or internal pilot sample size fraction is small). The blinded re-estimation method is preferred and suggested if possible.
2.In adaptive group sequential design, the sample size adjustment strategy in CHW method can be described as adjustment based on effect-size ratio and conditional power. The simulation results show that the difference of final sample size and power between two adjustment methods is obvious. In two stage adaptive group sequential design, the results from CHW method, CRP method and ACR method are approximately the same; if ACR method is used, when conditional power is between 0.3 and 0.8 at the first interim analysis, it is not necessary to adjust the final critical stopping boundary, and this strategy can control type I error better.
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