seqmon(seqmon)
seqmon()所属R语言包：seqmon
Sequential Monitoring of Clinical Trials
连续监测的临床试验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A program that computes the probabilities of crossing boundaries in a group sequential clinical trial. It implements the Armitage-McPherson and Rowe (1969) algorithm using the method described in Schoenfeld D. (2001). Assume that there is a sequence of test statistics z1,...zm in a clinical trial. Each statistic has a standard normal distribution under the null hypothesis. Let a1,...,am and b1,...,bm be a lower and an upper boundary and let t1,...,tm be the amount of information that was used to calculate each statistic. The function calculates the probability that zj<aj, j<=i and bj<zj, j<=i for i=1,...,m. Probabilities for an alternative hypothesis can be found by adding an offset to a1,...,am and b1,...,bm equal to the expected value of the statistic.
一个程序来计算的概率跨越边界的一组连续的临床试验。它实现阿米蒂奇麦弗逊式和Rowe使用舍恩菲尔德D.（2001）中记载的方法（1969）的算法。假设有一序列的检验统计量z1,...zm在临床试验中。每个统计标准正态分布的零假设下。让我们a1,...,am和b1,...,bm是一个上限和下限边界，让我们t1,...,tm是被用来计算每一个统计量的信息。该函数计算概率，zj<aj, j<=i和bj<zj, j<=ii=1,...,m。备择假设的概率可以通过添加一个偏移量a1,...,am和b1,...,bm等于预期值的统计信息。
用法----------Usage----------
seqmon(a,b,t,int)
参数----------Arguments----------
参数：a
A vector of the lower boundaries at information time t1,t2,...,tm
的向量的下边界信息时刻t1，t2，...，TM
参数：b
A vector of the upper boundaries at the information times t1,t2,...tm
的向量的上边界的信息时间t1，t2时，... TM
参数：t
A vector of the information times, usually 1:m
信息时代的矢量，通常为1：M
参数：int
A vector of the number of intervals to divide up (a[k],b[k]), 500 should be adequate
划分（一个[k]的，B [k]的），500应该是足够的间隔数的向量
值----------Value----------
A m x 2 matrix giving the the cumulative probabilities of crossing the lower boundary and the probabilities of crossing the upper boundary.
一个×2矩阵的累积概率交叉的下边界和上边界穿越的概率。
注意----------Note----------
The test statistic is assumed to be standardized so that it's value at any point in time is normally distributed random variable with mean zero and variance one. The numerator of the test
假设的检验统计量是标准化的，所以，它的值在任何时间点通常是分布的随机变量具有零均值和方差1。测试的分子
参考文献----------References----------
Armitage, P., McPherson, C. K. and Rowe, B. C. (1969) “Repeated significance tests on accumulating data” Journal of the Royal Statistical Society, Series A, General, 132, 235-244
Schoenfeld D. (2001) “A simple Algorithm for Designing Group Sequential Clinical Trials” Biometrics 27 , pp, 972-974.
O'Brien, Peter C. , and Fleming, Thomas R. (1979), “A multiple testing procedure for clinical trials”, Biometrics, 35 , 549-556
实例----------Examples----------
##The following gives the probablility of crossing [＃以下给出了交叉probablility]
##the boundaries for a O'Brien Flemming (1979) lower and upper bound[＃界限的奥布莱恩弗莱明（1979）的上限和下限]
##with five looks at the data under the null hypothosis.[＃五个看起来在空hypothosis下的数据。]
z<-2.04
a<- -z*sqrt(5/(1:5))
b<- +z*sqrt(5/(1:5))
t<- 1:5
int<- 500* array(c(1),5)
seqmon(a,b,t,int)
##This gives the probabilities under the alternative hypothesis if the expected value of the mean difference over it's[这使备择假设下的概率，如果超过它的预期中值的平均差异]
##standard error for one group is 1.5[＃标准误差为一组为1.5]
u<-1.5
seqmon(a+u*sqrt(1:5),b+u*sqrt(1:5),t,int)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 01:27:01
