个体生物等效性检验的样本含量估计

目的:对两种设计方法、三种检验方法的个体生物等效性的检验效能进行比较,并估计样本含量。方法:采用Monte-Carlo模拟研究。结果:2×4交叉设计所需的样本含量低于2×3设计。在个体内变异小于0.2时,可以采用估计法进行样本含量的估计;在个体内变异接近0.2时,可以采用检验法进行样本含量的估计;在个体内变异大于0.3时,可以选任一方法(估计法和检验法)估计样本含量,并选择合适的方法进行样本含量的估计。结论:个体生物等效性的样本含量因不同的个体内变异和个体与药物间的交互作用、设计而不同。     

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。     

统计学上影响样本量的条件

<正>一般来说,统计学上样本含量估算取决于四个要素:(1)假设检验的Ⅰ类错误概率α的大小,Ⅰ类错误概率α越小,所需样本含量越大。(2)假设检验的Ⅱ类错误概率β或检验效能1-β的大小。一般要求检验效能最好在0.80及以上,Ⅱ类错误概率β越小,所需样本含量越大。(3)总体间差值δ的大小。如两总体均数的差值δ=|μ1-μ2|,或两总体率的差值δ=|π1-π2|。δ值越小,所需样本含量越大。若研究者无法获得δ的信息,可通过查阅文献     

单因素重复测量设计样本含量的估算及不同计算方法之间的比较

目的:介绍并比较几种重复测量设计样本含量的估算方法。方法:通过实例分析,分别采用PASS 11,stata统计学软件以及相关的计算公式计算其所需样本含量。结果:对同一案例,PASS 11软件中Compound Symmetry法需27例,AR法需19例,Banded法需12例,Simple法需6例;Stata软件中post法需27例,change法需13例,ancova法需10例;公式计算结果为14例。结论:PASS 11,stata统计学软件可以方便地用来估计重复测量设计的样本含量;正确分析研究设计性质并针对性地设置软件中对应参数是获得合适估计结果的关键。     

精度法估计OR或RR研究中的样本含量

提出了在确定可信区间概率时选择较高精度估计病例对照研究中 OR和队列研究中 RR所需样本含量的公式。由公式计算的样本含量能使研究者了解由样本所获得的点估计值接近总体真实值的程度 ,从而能较为准确地判断研究结果的实际意义。     

非甾体抗炎药不良反应队列研究的样本量估计

目的：估算非甾体抗药不良反应队列研究的样本量，为临床科研设计提供依据。方法：根据研究目的及统计分析计划选择样本量的估算方法，多种估算方法相结合，提出几种方案以供决策和选择。结果：依据文献资料数据以及研究需要解决的问题，计算并给出了多前提条件下的最小观测例数。结论：在考虑样本增量的条件下，根据本研究的具体情况确定每一平行组观测例数为150例，并以此为依据进行实验设计。     

配对二项数据等效性／非劣效性评价的样本含量估计和假设检验

新药及医疗器械临床试验中，有时会涉及到两比较组采用配对设计获得的二项反应数据（配对二项数据）的等效性／非劣效性问题。两独立组率之间等效性／非劣效试验的样本含量估计及假设检验方法已较为成熟，但对于配对二项数据两组率之间的等效性／非劣效性试验的样本含量估计及假设检验方法还应用不多。本文介绍了一种渐进的基于约束极大似然估计的方法用于配对二项数据两组率之间的等效性／非劣效性试验的样本含量估计和假设检验，借助一个超声诊断仪临床试验的例子阐明了本方法的应用，还就有关实际问题进行了讨论。     

科研过程中样本含量的估计方法

<正> 医学科研中时常遇到要想按设计要求搜集完所需样本含量要花很长时间,研究者有时希望在某个时点上停下来,予计一下结果,特别当调查是冗长乏味的时候,这种希望会很强烈,由于随机变异的关系样本含量太小又不易发现差别,到底在什么时候停下来合适呢?一种最好的解决办法是边搜集边分析,一旦达到差异显著的结果就停止搜集资料,     

成组序贯试验在临床试验中的应用

所谓临床试验（ｃｌｉｎｉｃａｌｔｒｉａｌ）是指任何一种有病人参加的有计划的试验,其目的是寻求在相同条件下对未来病人的一种最合适的治疗方法。而多中心试验（ｍｕｌｔｉｃｅｎｔｒｅｔｒｉａｌｓ）是临床试验中经常使用的一种方法,它是由一个或几个主要研究者总负责,多个单位的研究者合作,按同一个试验方案同时进行的临床试验。在一个持续时间较长的临床试验中可按要求进行期中分析（ｉｎｔｅｒｉｍａｎａｌｙｓｉｓ）。在临床试验设计中常用的试验设计方案有平行组设计（ｐａｒａｌｌｅｌｇｒｏｕｐｄｅｓｉｇｎ）、交叉设计（ｃｒｏ…     

指导临床合理用药的抗心律失常药现行分类

过去20多年,根据化学结构、细胞内及整体心脏的电生理特性,曾提出抗心律失常药物的几种分类法。大多数分类无助于药物的应用,并且在临床医生和研究者间引起一些争议。直至1969年,Vaughn Williams 提出抗心律失常药四类分类法,才得到广泛承认(表1)。1979年,作者根据大量电生理学的资料,对归属Vauglm Williams Ⅰ型的许多新药作进一步分类,并     

On Sample Size Calculation in Bioequivalence Trials

Sample size calculation plays an important role in bioequivalence trials. In practice, a bioequivalence study is usually conducted under a crossover design or a parallel design with raw data or log-transformed data. In this paper, we discuss the differences in sample size calculation between a crossover design and a parallel design with raw data or log-transformed data. Formulas for sample size calculation under a crossover design and a parallel design with raw data or log-transformed data are derived. A brief discussion for the relationship among these formulas is given.     

ON SAMPLE SIZE CALCULATION BASED ON ODDS RATIO IN CLINICAL TRIALS

Sample size calculation formulas for testing equality, noninferiority, superiority, and equivalence based on odds ratio were derived under both parallel and one-arm crossover designs. An example concerning the study of odds ratio between a test compound (treatment) and a standard therapy (control) for prevention of relapse in subjects with schizophrenia and schizoaffective disorder is presented to illustrate the derived formulas for sample size calculation for various hypotheses under both a parallel design and a crossover design. Simulations were performed to assess the adequacy of the sample size calculation formulas. Simulation results were given at the end of the paper.     

On sample size calculation based on odds ratio in clinical trials

Sample size calculation formulas for testing equality, noninferiority, superiority, and equivalence based on odds ratio were derived under both parallel and one-arm crossover designs. An example concerning the study of odds ratio between a test compound (treatment) and a standard therapy (control) for prevention of relapse in subjects with schizophrenia and schizoaffective disorder is presented to illustrate the derived formulas for sample size calculation for various hypotheses under both a parallel design and a crossover design. Simulations were performed to assess the adequacy of the sample size calculation formulas. Simulation results were given at the end of the paper.     

Tests for inter-subject and total variabilities under crossover designs

In this paper, we consider statistical tests for inter-subject and total variabilities between treatments under crossover designs. Since estimators of variance components for inter-subject variability and total variability in crossover design are not independent, the usual F-test cannot be applied. Alternatively, we propose a test based on the concept of the extension of the modified large sample method to compare inter-subject variability and total variability between treatments under a 2 x 2 m replicated crossover design. An asymptotic power of the proposed test is derived. A sensitivity analysis is performed based on the asymptotic power to determine how the power changes with respect to various parameters such as inter-subject correlation and intra-class correlation. Also the two methods for sample size calculation for testing total variability under 2 x 4 crossover design are discussed. The method based on the Fisher-Cornish inversion shows better performance than the method based on the normal approximation. Several simulation studies were conducted to investigate the finite sample performance of the proposed test. Our simulation results show that the proposed test can control type I error satisfactorily.     

DROPOUTS IN LONGITUDINAL STUDIES: DEFINITIONS AND MODELS

In this paper, we consider statistical tests for inter-subject and total variabilities between treatments under crossover designs. Since estimators of variance components for inter-subject variability and total variability in crossover design are not independent, the usual F-test cannot be applied. Alternatively, we propose a test based on the concept of the extension of the modified large sample method to compare inter-subject variability and total variability between treatments under a 2×2mreplicated crossover design. An asymptotic power of the proposed test is derived. A sensitivity analysis is performed based on the asymptotic power to determine how the power changes with respect to various parameters such as inter-subject correlation and intra-class correlation. Also the two methods for sample size calculation for testing total variability under 2×4 crossover design are discussed. The method based on the Fisher–Cornish inversion shows better performance than the method based on the normal approximation. Several simulation studies were conducted to investigate the finite sample performance of the proposed test. Our simulation results show that the proposed test can control type I error satisfactorily.     

Crossover Designs With Two Binary Endpoints

ABSTRACT

We propose a crossover design for simultaneous significance testing of two binary endpoints, in which the AB/BA crossover design is carried out for each endpoint. An asymptotic α-level test is obtained by applying the intersection-union principle to the marginal Mainland–Gart tests. Power approximations and sample size calculation are derived and implemented in R programs. An adaptive design with sample size reestimation is also presented. We demonstrate the numerical accuracy of the proposed design through an extensive simulation study. Supplementary materials for this article are available online.     

On statistical power for average bioequivalence testing under replicated crossover designs

In its recent guidance on bioequivalence, the U.S. Food and Drug Administration (FDA) recommends a two-sequence, four-period (2 x 4) replicated crossover design be used for assessment of population and individual bioequivalence [FDA. Guidance for Industry on Statistical Approaches to Establishing Bioequivalence; Center for Drug Evaluation and Research, Food and Drug Administration: Rockville, MD, 2001]. The recommended replicated crossover design not only allows estimates of both the inter-subject and the intra-subject variabilities and the variability due to subject-by-formulation interaction, but also provides an assessment of average bioequivalence (ABE). In this article, power function for assessment of ABE under a general replicated crossover design (i.e., a 2 x 2m replicated crossover design) based on the traditional analysis of variance model and the mixed effects model as suggested by the FDA are studied. It is found that the power of a 2 x 2m replicated crossover design depends upon the variability due to subject-by-formulation interaction and the number of replicates. Based on the derived power function, formula for sample size calculation for assessment of ABE under a 2 x 2m replicated crossover design is also provided.     

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.     

Sample Size Determinations for the Wilcoxon–Mann–Whitney Test: A Comprehensive Review

The Wilcoxon–Mann–Whitney (WMW) test is the most commonly used nonparametric method to compare two treatments when the underlying distribution of the outcome variable is not normally distributed. The sample size calculation methods for the WMW test have been extensively discussed in the literature. In this article we give a comprehensive review of sample size calculation methods for the WMW test on data with or without ties. We also provide detailed implementation of these sample size calculation methods for the WMW test depending on the characteristics of the data and the amount of available information. In addition, the implementation of these methods in popular sample size calculation software packages is also discussed. This article will be very helpful for researchers to determine sample sizes for the WMW test in the design phase of a study.     

Sample Size Calculations for Population Pharmacodynamic Experiments Involving Repeated Dichotomous Observations

Analysis of repeated binary measurements presents a challenge in terms of the correlation between measurements within an individual and a mixed-effects modelling approach has been used for the analysis of such data. Sample size calculation is an important part of clinical trial design and it is often based on the method of analysis. We present a method for calculating the sample size for repeated binary pharmacodynamic measurements based on analysis by mixed-effects modelling and using a logit transformation. Wald test is used for hypothesis testing. The method can be used to calculate the sample size required for detecting parameter differences between subpopulations. Extensions to account for unequal allocation of subjects across groups and unbalanced sampling designs between and within groups were also derived. The proposed method has been assessed via simulation of a linear model and estimation using NONMEM. The results showed good agreement between nominal power and power estimated from the NONMEM simulations. The results also showed that sample size increases with increased variability at a rate that depends on the difference in parameter estimates between groups, and designs that involve sampling based on an optimal design can help to reduce cost.