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M on te C arlo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。     

生存研究样本量测定引论

为论述生存研究样本量测定的基本原理和方法.将用于比例检验样本量测定的经典渐近正态法加以扩展以适应终检形成广义渐近正态法。结果显示,与已有校正终检样本量测定方法相比．该方法的特点是与生存率检验相匹配,摆脱了指数分布的假设,在无终检时还原为经典方法。该方法适用于癌症生存研究的设计。     

两生存率比较所需样本量的精确测定──逐个迭代法

将广义渐近正态法写成迭代形式，以逐个迭代实现样本量的分配，获得精确等样本或给定比例样本设计。该方法具有可逆性和还原性：由所得样本量逆运算可复得设计参数，在无终检时还原为经典方法。其功效高于简化等样本设计     

t检验的使用条件

正t-检验是比较两组均数差别最常用的方法,其可以简单的分为两类。1、配对t检验1)定义:所谓"配对"是指两样本中的个体两两对应,不可以独立颠倒顺序,否则会改变问题的性质。2)配对t检验(均数的显著性检验)适用范围:(1)自身比较,即同一受试对象前后测的比较;(2)用两种不同方法来测定一个样本中的两部分;(3)将配对组随机分成两组。2、两组独立样本的T检验1)定义:所谓"独立样本"是指两样本中的个体可以独立颠倒顺序而不对问题产生影响,也即非配对样本。2)注意:建立数据文件的方法与配对t检验不同。     

t检验的使用条件

<正>t-检验是比较两组均数差别最常用的方法,其可以简单的分为两类。1、配对t检验1)定义:所谓"配对"是指两样本中的个体两两对应,不可以独立颠倒顺序,否则会改变问题的性质。2)配对,检验(均数的显著性检验)适用范围:(1)自身比较,即同一受试对象前后测的比较;(2)用两种不同方法来测定一个样本中的两部分;(3)将配对组随机分成两组。2、两组独立样本的T检验1)定义:所谓"独立样本"是指两样本中的个体可以独立颠倒顺序而不对问题产生影响,也即非配对样本。2)注意:建立数据文件的方法与配对t检验不同。     

临床试验中剂量-反应率关系研究中的样本含量估计方法

目的：Ⅱ期临床试验中,剂量-反应率关系研究所需要的样本含量估计方法介绍及评价。方法：目前常用于二分类变量资料剂量-反应关系研究中样本量估计的方法包括Jun-Mo Nam提出的基于正态近似Cochran-Armitage趋势检验的估算方法和Chang提出的unified contrast估算方法,本文在假设反应率P在logit尺度下与剂量呈线性关系的前提下,分别用上述两种方法估算在不同的斜率和0剂量组反应率下所需的样本含量,并采用计算机模拟抽样技术评价不同样本含量所对应的检验效能。结果：当反应率P在logit尺度下与剂量呈线性关系时,在不同参数组合下,两种样本含量估算方法得到的结果均比较接近;Cochran-Armitage趋势检验正态近似法的模拟检验效能接近期望效能,而Chang＇s unified contrast方法的检验效能受对比系数的影响较大,若预设的对比系数的形状与实际反应率比较接近,则此时模拟得到的检验效能将高于设计时的检验效能。结论：当反应率P在logit尺度下与剂量呈线性关系时,若Chang＇s unified contrast方法中的对比系数的设定与反应率P形状相同时,两种样本含量估计方法基本一致。     

两样本比较的正态计分法的确切概率计算

正态计分检验法类似于两样本Wilcoxon秩和检验法,又称为Van der Waereen s检验法。本文对于秩变换作出一种第二次变换,称为正态计分变换,并将原始资料化成2×C有序列联表,导得了确切概率计算方法。     

t检验的使用条件

正t-检验是比较两组均数差别最常用的方法,其可以简单的分为两类。1、配对t检验1)定义:所谓"配对"是指两样本中的个体两两对应,不可以独立颠倒顺序,否则会改变问题的性质。2)配对检验(均数的显著性检验)适用范围:(1)自身比较,即同一受试对象前后测的比较;(2)用两种不同方法来测定一个样本中的两部分;(3)将配对组随机分成两组     

On two-sample McNemar test

Measuring a change in the existence of disease symptoms before and after a treatment is examined for statistical significance by means of the McNemar test. When comparing two treatments, Feuer and Kessler (1989) proposed a two-sample McNemar test. In this article, we show that this test usually inflates the type I error in the hypothesis testing, and propose a new two-sample McNemar test that is superior in terms of preserving type I error. We also make the connection between the two-sample McNemar test and the test statistic for the equal residual effects in a 2 × 2 crossover design. The limitations of the two-sample McNemar test are also discussed.     

A NONPARAMETRIC PROCEDURE OF THE SAMPLE SIZE DETERMINATION FOR SURVIVAL RATE TEST

Inthesurvivalstudiesofchronicdiseases,themostfrequentlyusedmethodsofdataanalysisarethenonparametricestimationsandcomparisonsofsurvivalratesatdifferenttimesbuttherehavebeennomatchedmethodsfordeterminingtherequiredsamplesizesofar.Insteadhavebeenoftenusedtheparametricmethodsbasedontheassumptionofexponentialdistributions[1～7]suchthattherelevantsamplesizesmaynotbesatisfiedwiththeprescribedpower.Thispaperreportsanonparametricprocedureofthesamplesizedeterminationforsurvivalratetest.1　SurvivalRateT…     

Sample sizes based on exact unconditional tests for phase II clinical trials with historical controls

Investigated in the setting of phase II clinical trials is the two-sample binomial problem of testing H0: pe = pc H1: pe > pc, where pe and pc are the unknown target population response rates for the experimental and control groups, respectively, using the usual Z-statistic with pooled variance estimator. The cornerstones that make this paper unique are as follows. First, the emphasis is on determining the sample size given that the control group information has already been collected (historical control). Second, exact unconditional inference, rather than an asymptotic method, is utilized. Sample size tables, contrasting the exact and asymptotic methods, are provided. Although asymptotic results were usually fairly close to the exact results, some important differences were observed.     

Homogeneity Test of Difference Between Two Correlated Proportions in Stratified Matched-Pair Studies

Stratified matched-pair studies are often designed for adjusting stratification factors in modern medical researches. This article investigates a homogeneity test of differences between two correlated proportions in stratified matched-pair studies. We propose three test procedures, including an asymptotic test, bootstrap test, and multiple comparison procedures, and determine sample size requirements for such tests in a stratified matched-pair study. Simulation studies are conducted to evaluate the performance of the three test procedures and the accuracy of our derived sample size formulas. Empirical results show that (1) the likelihood ratio statistic is robust, while the score statistic and the modified score statistic are conservative in some cases of our considered settings; (2) the likelihood ratio statistic and the score statistic with the bootstrap method and the MaxT procedure behave satisfactorily in the sense that their type I error rates are close to the pre-given significance level; and (3) the derived sample size formulas are rather accurate. A real example from a clinical laboratory study is used to illustrate the proposed methodologies.     

Planning survival studies to compare a treatment to an active control

Rubinstein et al (1) presented a procedure for determining the required duration of accrual for a clinical trial comparing the survival distributions of two treatments using a classical hypothesis testing formulation. Here their testing procedure is modified in two ways. First, the asymptotic variances used in computation of the probabilities of type I and type II errors are based on the values of the parameters specified by the null and alternative hypotheses, respectively. Second, the null hypothesis is modified for situations where it is desired to show that the experimental treatment is better or not much worse than the control.     

A note on sample size calculation for mean comparisons based on noncentral t-statistics

One-sample and two-sample t-tests are commonly used in analyzing data from clinical trials in comparing mean responses from two drug products. During the planning stage of a clinical study, a crucial step is the sample size calculation, i.e., the determination of the number of subjects (patients) needed to achieve a desired power (e.g., 80%) for detecting a clinically meaningful difference in the mean drug responses. Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design. Useful tables are constructed and some examples are given for illustration.     

A NOTE ON SAMPLE SIZE CALCULATION FOR MEAN COMPARISONS BASED ON NONCENTRAL t-STATISTICS

One-sample and two-sample t-tests are commonly used in analyzing data from clinical trials in comparing mean responses from two drug products. During the planning stage of a clinical study, a crucial step is the sample size calculation, i.e., the determination of the number of subjects (patients) needed to achieve a desired power (e.g., 80%) for detecting a clinically meaningful difference in the mean drug responses. Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design. Useful tables are constructed and some examples are given for illustration.     

A Comparison of Two-Sample Tests of Significance When Used With Variable Treatment Effects

This article compares the performance of many two-sample tests of significance that might be used to test the equality of means when the effect of the treatment is variable. Of the 19 tests that were compared, the normal scores test is recommended for general use in testing the null hypothesis of no treatment effect against the alternative that the distributions are stochastically ordered when the ratio of the larger standard deviation to the smaller standard deviation does not exceed 1.3. The Baumgartner-Weiß-Schindler tests and an adaptive test also have higher power than the pooled t-test, the unequal variance t-test, and the rank-sum test for many distributions. In the simulation studies, data in the first sample are generated from nine distributions, including long-tailed and skewed distributions. Data in the second sample are generated by adding a random treatment effect to a random variable that was generated from the same distribution that was used in the first sample. Because we restricted our power studies to treatment effects that are positive or zero, the population distributions will be stochastically ordered. The results of these studies demonstrate that the normal scores test is often more powerful than the t-tests and the rank-sum test. If the ratio of the standard deviations does exceed 1.3, then one of the t-tests is recommended.