抽样调查法和单组目标值法对诊断试验样本量计算差异的分析

探讨设计以验证灵敏度和特异度为目的 的诊断试验时,不同样本量计算方法间的区别.通过直观的样本量公式与计算结果比较,分析不同样本量计算方法间的差异,进一步通过Monte Carlo随机模拟方法,验证所得结果的正确性.抽样调查法计算所需的样本量明显低于单组目标值法,随机模拟显示,在相同的参数设置下,单组目标值法给出的样本量能够提供更高的研究把握度.两种样本量设计方法的适用条件不同,存在本质区别,研究者必须根据研究目的 设置相应参数,如果在以检验某种诊断方法的诊断能力是否不低于某个临床认可的标准时,按照单组目标值法设计的样本量,才能提供足够的检验把握度,证明新诊断方法的有效性.     

Monte Carlo抽样计算配对符号秩检验的确切概率

配对符号秩检验法(Wilcoxon matched-pairs signed-ranks test)是一种处理配对资料的非参数检验方法.由于其确切概率的经典算法[1,2]计算量非常大,所以传统的配对符号秩检验常常使用查表法或者正态近似法.但是,常用的医用统计书中的配对符号秩检验的界值表并不完全一致[1,2].而在样本量不够大时,正态近似效果不好.所以需要一种实际可行的确切概率的计算方法.     

基于负二项分布的确切推断在微核试验数据分析中的模拟研究

目的通过模拟研究对几种可用于小样本微核数据统计分析的方法进行讨论,为小样本微核数据的分析提供科学依据。方法用R软件进行编程模拟来估计不同参数下不同分析方法的Ⅰ类错误和检验功效。结果 Poisson精确概率法、Fisher精确概率法、负二项精确概率法、t检验及其变换、确切概率秩和检验的检验功效和Ⅰ型错误各不相同,其检验功效随δ和动物数n的增大而增大。其中Poisson精确概率法、Fisher精确概率法、负二项精确概率法的检验功效较高,但总体来自于负二项分布时,Poisson精确概率法、Fisher精确概率法的Ⅰ型错误较大,而负二项精确概率法Ⅰ型错误较小。结论在小样本微核数据分析中负二项精确概率法具有检验功效较高且犯Ⅰ型错误概率小的优越性。     

年龄调整率的区间估计及其在宫颈癌患病率估计中的应用

目的 评价三种年龄调整率可信区间估计方法,探索适合江苏省宫颈癌筛查研究中年龄调整患病率可信区间估计的方法.方法 以二项分布正态近似法、Gamma分布法及"确切概率法"进行年龄调整率的区间估计,运用统计模拟考察多种率及阳性数情况下三种方法的区间覆盖率及宽度.结果 当样本量较小(阳性数较少)时,确切概率法的覆盖率离理论可信度的偏差及区间宽度均优于Gamma分布法,两者的覆盖率均明显优于正态近似法;随着阳性数增多,三法各自的覆盖率偏差及区间宽度均逐渐变小,方法间的差异亦逐渐缩小;当阳性数增至30以上时,确切概率法及正态近似法的覆盖率的偏差皆在±1%以内,此时两者的区间宽度接近;而Gamma分布法的覆盖率偏差若要达到1%以内,则要求总阳性数在100以上.无论样本构成是轻度偏离还是明显偏离总体构成,上述规律皆成立.结论 综合考虑区间覆盖率、区间宽度及计算便捷性,建议当总阳性数小于30时,采用确切概率法计算调整率的可信区间;当总阳性数大于等于30时,采用正态近似法.     

正态近似法计算二项分布总体率95%可信区间的应用条件研究

目的对目前惯用的正态近似法计算总体率可信区间的应用条件进行评价,为正确应用该法提供理论基础和应用指导.方法应用二项分布原理计算总体率精确可信区间并与正态近似法计算结果相比较;采用蒙特卡洛模拟抽样评价可信度;应用SAS和Excel软件绘制二项分类数据概率分布图.结果以n×p=5作为近似条件,用正态近似法计算总体率可信区间可造成显著的相对误差.当n×p为常数时,随着p减小,相对误差在一定范围内呈线性增加;随着n增加,相对误差呈非线性增加.结论目前惯用的估计总体率可信区间的正态近似法应用条件并不能保证总体率估计的可信度和准确度.根据实验结果,提出了使用正态近似法估计总体率95%可信区间一套新的应用条件.     

一种基于条件把握度法和EM算法相结合的样本量再估计

目的探索期中分析时正态分布资料的样本量再估计。方法通过模拟试验比较揭盲状态下样本量再估计的条件把握度法及盲态下样本量再估计的EM算法,进而探索将两者相结合的应用策略。结果均数差异大于初始值时越早使用条件把握度的方法,再估计之效能越高。结论先用EM算法进行样本量再估计,如果再估计样本量大于初始样本量1.5倍,采用揭盲状态下的条件把握度法进行样本量再估计,可以尽量保持盲态和试验完整性并获得较高的把握度,同时也能减少一部分假阴性的发生以及临床资源的浪费。     

四格表资料三种常用统计方法的比较

本文应用计算机模拟方法比较了四格表资料三种常用统计方法，即ＰｅａｒｓｏｎＸ２检验（非校正Ｘ２检验）、Ｙａｔｅｓ校正Ｘ２检验和Ｆｉｓｈｅｒ精确概率法的第一类错误概率和把握度Ｓ结果显示ＰｅａｒｓｏｎＸ２检验较其他两种方法的第一类错误概率更接近检验水准，且其把握度更高，不受诸如理论频数小于５或样本含量不足４０等条件的限制，对此结论无根本影响。故提倡在任何场合下均可用ＰｅａｒｓｏｎＸ２检验分析国格表资料。     

四格表的判别式与显著性检验的合理步骤

四格表资料的显著性检验,通常采用X~2法或连续性校正X_c~2法,而精确概率法(P法)因计算较繁,非必要时不用。但一般统计书籍对何时采用X_c~2法与P法的规定并非很确切,以致有时出现X_c~2法矫枉过正的不合理现象。1984年董玉恒提出制定统一的检验步骤,他根据X~2与X_c~2两值大小关系不同,提出三种不同步骤,确比过去合理,但较繁杂。本文从研究四格表数字组合的概率入手(仅讨论双侧检验),建     

率的u检验样本含量计算图

本图可用以计算率的两种类型各两种把握度 u 检验(正态近似法)的样本含量。两种类型是样本率和总体率比较,两样本率比较;两种把握度是 1-β=0.5(一般检验),1-β=0.05(最佳检验)。     

完全随机设计两样本比较的非参数方法的检验功效比较

目的对完全随机设计时两样本比较的Wilcoxon、Kruskal—Wallis及Median三种方法的检验功效进行比较。方法用SAS9.13软件编程，采用Monte Carlo方法。设置数据不同的分布类型、样本量、样本量比率及方差齐性与否条件，比较三种方法的检验功效。结果正态分布，方差不等较之方差相等三法的功效高。方差相等时，偏态分布较之正态分布功效高。当分布为偏态，方差相等较之方差不等功效高。Kruskal—Wallis法在小样本时（n≤30）功效高于Wilcoxon法，n大于30时两者近似相等；中位数法功效低于其他两法，但在样本量n=100，且效应量较大（ES=0．8）时，其功效接近其他两法。结论样本量较小时（n≤30）建议采用Kruskal—Wallis法，样本量n〉30时，Kruskal—Wallis和Wilcoxon法均可，样本量大于n〉100时，三种方法均可采用。     

随机模拟法验证非劣效临床试验样本量计算公式

目的探讨并验证非劣效临床试验样本量计算方法。方法通过理论公式的推导，得到非劣效临床试验样本量计算公式，并用随机模拟的方法，使用该公式计算出的样本量估计实际的检验效能，以验证公式的正确性。结果由概率论严格推导得到样本量计算公式，并通过SAS随机模拟宏程序验证了公式的正确性，即模拟出的检验效能与最初带入公式计算时设定的预期的检验效能一致。结论样本量计算与临床试验设计有机结合的方法，解决了现行临床试验样本量计算方法与研究设计脱节的问题。     

率的非劣效试验中样本含量的估计——SAS和PASS实现

目的 提供二分类定性资料平行设计非劣效临床试验样本含量最常用的计算公式及其 SAS和PASS过程,并为相关参数的设置提供参考。方法 基于二项分布的正态近似理论推导样本含量的估计公式,通过SAS程序和PASS过程探讨各重要参数（样本率、非劣效界值）变化时样本含量及检验效能的变化情况。结果 对率的非劣效试验样本含量的计算,公式、SAS程序和PASS过程能得到一致结果;当检验水准和对照组样本率确定时,试验组样本率越大、检验效能越小、界值越大,所需样本含量越小。结论 利用本文提供的公式、SAS程序和PASS过程,可以帮助研究者系统快速得到二分类资料2组平行非劣效设计时的样本含量。试验组样本率、检验效能和非劣效界值是非劣效临床试验估计样本含量必须认真考虑的参数。     

Statistical approaches to establishing vaccine safety

In a vaccine safety trial, the primary interest is to demonstrate that the vaccine is sufficiently safe, rejecting the null hypothesis that the relative risk of an adverse event attributable to the new vaccine is above a prespecified value, greater than one. We evaluate the exact probability of type I error of the likelihood score test, with sample size determined by normal approximation, by enumeration of the binomial outcomes in the rejection region and show that it exceeds the nominal level. In the case of rare adverse events, we recommend the Poisson approximation as an alternative and develop the corresponding conditional and unconditional tests. We give sample size and power calculations for these tests. We also propose optimal randomization strategies which either (i) minimize the total number of adverse cases or (ii) minimize the expected number of subjects when the vaccine is unsafe. We illustrate the proposed methods using a hypothetical vaccine safety study.     

单样本率确切概率检验的样本量与检验效能非单调变化关系的研究

目的探究单样本率确切概率检验样本量估算非单调性的原因,通过SAS编程纠正其非单调性,并实现先设定检验效能后估算样本量的功能。方法从二项分布的离散性入手,分析检验效能与样本量非单调变化的原因,编写SAS宏程序实现计算功能。结果单样本率确切概率检验中,样本量与检验效能呈锯齿状非单调变化关系,这种现象由离散概率分布的实际检验水准常低于检验前所设定的理论检验水准所致。结论我们提出了纠正单样本率确切概率检验样本量估算的非单调性方法 ,即对于所有满足设定检验效能的样本量,找到一个不中断序列中的最小值,就是所估算的样本量。根据这一思路,本研究还解决了先设定检验效能后估算样本量的问题。     

Sample size determination in clinical trials with multiple co‐primary binary endpoints

Clinical trials often employ two or more primary efficacy endpoints. One of the major problems in such trials is how to determine a sample size suitable for multiple co‐primary correlated endpoints. We provide fundamental formulae for the calculation of power and sample size in order to achieve statistical significance for all the multiple primary endpoints given as binary variables. On the basis of three association measures among primary endpoints, we discuss five methods of power and sample size calculation: the asymptotic normal method with and without continuity correction, the arcsine method with and without continuity correction, and Fisher's exact method. For all five methods, the achieved sample size decreases as the value of association measure increases when the effect sizes among endpoints are approximately equal. In particular, a high positive association has a greater effect on the decrease in the sample size. On the other hand, such a relationship is not very strong when the effect sizes are different. Copyright © 2010 John Wiley & Sons, Ltd.     

Modified exact sample size for a binomial proportion with special emphasis on diagnostic test parameter estimation

The design of epidemiologic studies for the validation of diagnostic tests necessitates accurate sample size calculations to allow for the estimation of diagnostic sensitivity and specificity within a specified level of precision and with the desired level of confidence. Confidence intervals based on the normal approximation to the binomial do not achieve the specified coverage when the proportion is close to 1. A sample size algorithm based on the exact mid-P method of confidence interval estimation was developed to address the limitations of normal approximation methods. This algorithm resulted in sample sizes that achieved the appropriate confidence interval width even in situations when normal approximation methods performed poorly.     

Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events

For rare outcomes, meta-analysis of randomized trials may be the only way to obtain reliable evidence of the effects of healthcare interventions. However, many methods of meta-analysis are based on large sample approximations, and may be unsuitable when events are rare. Through simulation, we evaluated the performance of 12 methods for pooling rare events, considering estimability, bias, coverage and statistical power. Simulations were based on data sets from three case studies with between five and 19 trials, using baseline event rates between 0.1 and 10 per cent and risk ratios of 1, 0.75, 0.5 and 0.2. We found that most of the commonly used meta-analytical methods were biased when data were sparse. The bias was greatest in inverse variance and DerSimonian and Laird odds ratio and risk difference methods, and the Mantel-Haenszel (MH) odds ratio method using a 0.5 zero-cell correction. Risk difference meta-analytical methods tended to show conservative confidence interval coverage and low statistical power at low event rates. At event rates below 1 per cent the Peto one-step odds ratio method was the least biased and most powerful method, and provided the best confidence interval coverage, provided there was no substantial imbalance between treatment and control group sizes within trials, and treatment effects were not exceptionally large. In other circumstances the MH OR without zero-cell corrections, logistic regression and the exact method performed similarly to each other, and were less biased than the Peto method.     

A noniterative sample size procedure for tests based on t distributions

A noniterative sample size procedure is proposed for a general hypothesis test based on the t distribution by modifying and extending Guenther's 6 approach for the one sample and two sample t tests. The generalized procedure is employed to determine the sample size for treatment comparisons using the analysis of covariance (ANCOVA) and the mixed effects model for repeated measures in randomized clinical trials. The sample size is calculated by adding a few simple correction terms to the sample size from the normal approximation to account for the nonnormality of the t statistic and lower order variance terms, which are functions of the covariates in the model. But it does not require specifying the covariate distribution. The noniterative procedure is suitable for superiority tests, noninferiority tests, and a special case of the tests for equivalence or bioequivalence and generally yields the exact or nearly exact sample size estimate after rounding to an integer. The method for calculating the exact power of the two sample t test with unequal variance in superiority trials is extended to equivalence trials. We also derive accurate power formulae for ANCOVA and mixed effects model for repeated measures, and the formula for ANCOVA is exact for normally distributed covariates. Numerical examples demonstrate the accuracy of the proposed methods particularly in small samples.     

Size and power estimation for the Wilcoxon-Mann-Whitney test for ordered categorical data

We derive an exact variance method for the size and power calculation for the Wilcoxon-Mann-Whitney test for ordered categorical data. The O'Brien-Castelloe approximation implemented in SAS version 9.2 (SAS Institute Inc., Cary, NC, USA) is simplified. Simulation studies show that the exact variance approach tends to be more accurate than the O'Brien-Castelloe approximation and the Zhao-Rahardja-Qu method derived under local alternatives.