非虚假设Ⅳ期临床试验的设计与分析

目的：论述非虚假设Ⅳ期临床试验的设计与分析。方法：按治疗-对照差与最小可识别差量的比较建立非虚假设。将临床试验的每个中心看作一个层，以层样本分数为权作加权平均得综合治疗-对照差及其期望和方差。由此构造非虚假设分层设计基本关系式，进而推导出非虚假设Ⅳ期临床试验所需样本量和检验统计量。以Monte Carlo方法展示其行为。结果：当最小可识别差量取零时，它还原为传统的虚假设Ⅳ期临床试验所需样本量和检验统计量，其观测功效与预定功效吻合。结论：这种临床试验可用于建立试药对于有效对照的临床优效性或非劣效性。     

非虚假设多中心配对临床试验

目的:将配对设计加以扩展,以适应非虚假设和分层。方法:将多中心临床试验的每个中心看作1层,取层样本分数为权计算平均治疗-对照差。将其期望与最小可识别差量比较建立非虚假设,按其方差构造分层配对设计基本关系式,进而推导出样本量公式,检验统计量,和功效函数。将这些用于临床试验的设计、执行和分析。以Monte Carlo方法展示观测功效。结果:这些在最小可识别差量取零时还原为对应经典统计学方法,其观测功效和期望功效吻合,所需样本量小于对应的两组设计。结论:这种临床试验直观高效,适于建立试药对于对照药的临床优效性、非劣效性或等效性,并附有实例描述用法。     

非虚假设综合渐近正态法

目的:提出非虚假设综合渐近正态法。方法:将渐近正态法加以扩展以覆盖非虚假设和分层设计,从而推导出单样本和两样本基本关系式,功效函数,样本量,及检验统计量。结果:当最小感知差别取零时,它还原对应经典方法,包括经典Cochran检验和Mantel-Haenszel检验。当层数取1时,它还原为非虚假设渐近正态法,包括Dunnett-Gent检验。当最小感知差别取零且层数取1时,它还原为经典渐近正态法,包括单样本和两样本比例检验。结论:这种方法可用于分层设计的有效对照临床试验,以建立临床优效性,非劣效性,或等效性。     

生物等效性研究引论

依据治疗 -对照差与最小临床承认疗效差量的比较推导出单样本非虚假设检验及其功效函数 ,以 Monte Carlo方法展示了其观测功效。结果显示 ,由所测样本量产生的观测平均 1“年”生存率与对应设计参数取值相吻合 ,观测功效与预定功效相吻合     

Log rank检验所需样本量的测定:迭代非中心法

目的:提出一种迭代非中心法,用于Log rank检验所需样本量的测定,并同Lachin-Foukes法进行了比较。方法:预置样本量重复抽样,计算Log rank检验统计量及其平均值。调整预置样本量重复操作,当该平均值充分逼近预定非中心参数时,最后预置样本量被看成是所需样本量。结果:该法所得样本量因生存分布而异,可满足Log rank检验预定功效。相比之下,Lachin-Foukes法所得样本量偏小,用于Log rank检验功效不足。结论:迭代非中心法优于Lachin-Foukes法,可用于慢性病和癌症生存研究的设计。     

临床试验中平行组设计二分类指标样本量的计算

临床试验中所需病例数应符合统计学要求,以确保对所提出的问题给予可靠的回答。样本的大小通常以试验的主要指标来确定,同时应考虑试验设计类型、比较类型等。针对优效/非劣效/等效性试验的目的及统计假设检验和方差,文中介绍了二分类指标平行组试验设计样本量的计算方法和通用公式,并结合临床试验的实际案例对样本量计算进行了应用分析。     

随机自适应设计的统计模拟

自适应设计是允许在不破坏试验有效性与安全性的前提下,通过临床中期分析,来发现和更改试验设计之初不合理的假设,降低研发成本,缩短研究周期。本研究借助matlab软件及蒙特卡罗方法,模拟优效性试验中两个治疗组(试验组与控制组)主要终点治疗指标的变化,包括组间差异值,方差,分配比,变异系数及样本容量的取值变化,观察对检验功效的影响。并通过模拟确定临床Ⅱ期实验中所需的最低样本容量。     

脉络通胶囊联合瑞舒伐他汀对冠心病经皮冠状动脉介入术后患者的临床疗效研究

目的:研究脉络通胶囊联合瑞舒伐他汀对冠心病经皮冠状动脉介入(PCI)术后患者的临床疗效.方法:本研究为前瞻性随机对照试验,选取2018年3月～2019年12月于本院行PCI术治疗的冠心病患者作为研究对象,通过优效性检验样本量计算确定样本量.结合文献资料预计所需样本量为54例/组,合计108例.考虑到10％的脱落情况,共...     

Log rank检验的功效

目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计算其观测功效,然后作对比分析。结果:所得观测功效在多数试验集均低于期望功效。与上半部相比,寿命表下半部期望和观测功效均较低。所得观测功效在不同终检水平或不同生存分布各不相同。结论:L ach in-Fou lkes法产生的样本量偏小,不能满足L og rank检验的预定功效。L og rank检验所需样本量因终检水平、生存时间或生存分布而异,L ach in-Fou lkes法无视这些事实,无法作出切合实际的测定,因此必须寻求与这种检验匹配的样本量测定方法。     

相对率的非劣效性试验检验效能及样本量的模拟计算方法及SAS实现

目的 :当评价指标为定性指标时 ,利用相对率作为标准判断非劣效性是一种方便易行的方法 ,本文目的在于解决这一背景下的样本量计算问题。方法 :利用随机模拟方法 ,在SAS下编写宏 ,估算样本量。结果 :设计出两个宏 ,分别计算给定样本量时的检验效能和限定检验效能时的样本量。结论 :探索出一条在临床研究中利用随机模拟方法估算样本量的途径 ,为类似问题的解决提供了参考     

Conservative Sample Size Estimation in Nonparametrics

Due to the uncertainty of the results of phase II trials, underpowered phase III trials are often planned. In recent literature the conservative approach for sample size estimation was proposed. Some authors, in the parametric framework, make use of the lower bound of the effect size for conservatively estimating the true power, and so the sample sizes. Here, we present a general bootstrap method for conservatively estimating, on the basis of phase II data, the sample size needed for a phase III trial. The method we propose is based on the use of nonparametric lower bounds for the true power of the test. A wide study is shown for comparing the performances of the new method in estimating the power of the Wilcoxon rank-sum test with those given by standard techniques based on the asymptotic normality of the test statistic. Results indicate that when the phase II sample size is around the ideal sample size for the phase III, the bootstrap provides better results than the other techniques. Since the method is general, it could be used for planning clinical trials for testing superiority, for testing noninferiority, and for more complicated situations, e.g., for testing multiple endpoints.     

Conservative sample size estimation in nonparametrics

Due to the uncertainty of the results of phase II trials, underpowered phase III trials are often planned. In recent literature the conservative approach for sample size estimation was proposed. Some authors, in the parametric framework, make use of the lower bound of the effect size for conservatively estimating the true power, and so the sample sizes. Here, we present a general bootstrap method for conservatively estimating, on the basis of phase II data, the sample size needed for a phase III trial. The method we propose is based on the use of nonparametric lower bounds for the true power of the test. A wide study is shown for comparing the performances of the new method in estimating the power of the Wilcoxon rank-sum test with those given by standard techniques based on the asymptotic normality of the test statistic. Results indicate that when the phase II sample size is around the ideal sample size for the phase III, the bootstrap provides better results than the other techniques. Since the method is general, it could be used for planning clinical trials for testing superiority, for testing noninferiority, and for more complicated situations, e.g., for testing multiple endpoints.     

Symmetry in square contingency tables: tests of hypotheses and confidence interval construction.

Bowker's test, a generalization of McNemar's test, performs well under the hypothesis of symmetry, but the estimator of variance used in the test is biased when the table is asymmetric and this calls into question the test's performance in non-null situations. We seek an alternative to Bowker's test in search of methods for simultaneous inference that are valid when the hypothesis of symmetry is false. We apply multivariate normal theory to develop chi-square tests and simultaneous confidence intervals for inferences concerning symmetry in k X k contingency tables. We propose a modified Wald statistic as a competitor to Bowker's test. We also proffer quadratic estimators of confidence intervals. In large samples, the recommended test statistic rejects the null hypothesis at the stated level of significance when the null hypothesis is true and always rejects with greater power than Bowker's test. The proffered interval estimators provide good simultaneous coverage of the pairwise differences between the population proportions at the stated confidence level.     

SYMMETRY IN SQUARE CONTINGENCY TABLES: TESTS OF HYPOTHESES AND CONFIDENCE INTERVAL CONSTRUCTION

Bowker's test, a generalization of McNemar's test, performs well under the hypothesis of symmetry, but the estimator of variance used in the test is biased when the table is asymmetric and this calls into question the test's performance in non-null situations. We seek an alternative to Bowker's test in search of methods for simultaneous inference that are valid when the hypothesis of symmetry is false. We apply multivariate normal theory to develop chi-square tests and simultaneous confidence intervals for inferences concerning symmetry in k × k contingency tables. We propose a modified Wald statistic as a competitor to Bowker's test. We also proffer quadratic estimators of confidence intervals. In large samples, the recommended test statistic rejects the null hypothesis at the stated level of significance when the null hypothesis is true and always rejects with greater power than Bowker's test. The proffered interval estimators provide good simultaneous coverage of the pairwise differences between the population proportions at the stated confidence level.     

Sample size adaptation in fixed-dose combination drug trial

Statistical testing in clinical trials can be complex when the statistical distribution of the test statistic involves a nuisance parameter. Some type of nuisance parameters such as standard deviation of a continuous response variable can be handled without too much difficulty. Other type of nuisance parameters, specifically associated with the main parameter under testing, can be difficult to handle. Without knowledge of the possible value of such a nuisance parameter, the maximum type I error associated with testing the main parameter may occur at an extreme value of the nuisance parameter. A well known example is the intersection-union test for comparing a combination drug with its two component drugs where the nuisance parameter is the mean difference between the two components. Knowledge of the possible range of value of this mean difference may help enhance the clinical trial design. For instance, if the interim internal data suggest that this mean difference falls into a possible range of value, then the sample size may be reallocated after the interim look to possibly improve the efficiency of statistical testing. This research sheds some light into possible power advantage from such a sample size reallocation at the interim look.     

Sample Size Computations for PK/PD Population Models

We describe an accurate, yet simple and fast sample size computation method for hypothesis testing in population PK/PD studies. We use a first order approximation to the nonlinear mixed effects model and chi-square distributed Wald statistic to compute the minimum sample size to achieve given degree of power in rejecting a null hypothesis in population PK/PD studies. The method is an extension of Rochon’s sample size computation method for repeated measurement experiments. We compute sample sizes for PK and PK/PD models with different conditions, and use Monte Carlo simulation to show that the computed sample size retrieves the required power. We also show the effect of different sampling strategies, such as minimal, i.e., as many observations per individual as parameters in the model, and intensive on sample size. The proposed sample size computation method can produce estimates of minimum sample size to achieve the desired power in hypothesis testing in a greatly reduced time than currently available simulation-based methods. The method is rapid and efficient for sample size computation in population PK/PD study using nonlinear mixed effect models. The method is general and can accommodate any type of hierarchical models. Simulation results suggest that intensive sampling allows the reduction of the number of patients enrolled in a clinical study.     

Two-Arm Comparisons in Two-Stage Designs for Stratified Randomized Phase II Trials

Abstract

Two-stage designs have been widely used in phase II clinical trials to evaluate whether a new treatment shows sufficient evidences of effectiveness to justify being tested in a phase III trial. The common primary endpoint in phase II trials is a binary response, such as tumor response. The distribution of patients’ response for a phase II clinical trial is often heterogeneous, making it desirable to stratify patients into subgroups according to different prognostic factors. In this article, for a two-arm stratified randomized phase II clinical trial, we consider two-stage designs and propose three testing procedures to compare the response rates between two treatments. The three procedures are based on different types of criteria, namely, the weighted average of the stratum-specific differences between treatment response rates, the estimated relative risk and odds ratio under the assumption of a common odds ratio over the strata. We consider conditional approach and present a simulation-based algorithm by modifying the algorithm in London and Chang to determine the parameters in designs for achieving the desired power at the nominal level. A numerical study is conducted to investigate the performance of the proposed procedure. Simulation results show that the split-levels of Type I and Type II errors and randomization ratio have a crucial impact on the overall sample size required. Decreasing the split-level or increasing the randomization ratio at the first-stage can result in a smaller total sample size if early termination after the first-stage does not occur. In terms of the total sample size required, the INVAR-weighted test outperforms the other tests when the odds ratio or the true difference between two response rates is constant across strata. When neither odd ratio nor the difference between two response rates is constant across the strata, the INVAR-weighted test also performs well when the randomization ratio for the first stage is large.     

A practical comparison of group-sequential and adaptive designs

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.