sampleSizeContourPlots(clippda)
sampleSizeContourPlots()所属R语言包：clippda
A function to construct a grid with contours for calculating sample size in multi dimensional
一个函数构造多维计算样本大小与轮廓网格
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function draws a grid for calculating the sample size based on the clinically important values of variances versus differences. Based on the analysis of data from past proteomic profiling studies of cancer, we define the clinically important parameters as the summary statistics of the intensities of the peaks with medium biological variation. On the grid, you may display the parameter values from a wide range of real-life data from past proteomic profiling studies, including: data from urine and serum samples of early- and late-stage colorectal cancer patients; serum samples of colorectal cancer patients assayed on four SELDI chip-types (IMAC, H50, Q10 and CM10); plasma samples from Limanda limanda fish; and urine samples of colorectal cancer patients analysed using both SELDI and MALDI sample processing protocols. These values may be used as guidelines for choosing the sample size calculation parameters. If your study involves profiling samples from late-stage disease or sera assayed on the IMAC chip, then the sample size is probably a value close to that of the outer left contour. The urine profiling studies require more samples to detect differences and the value of the contours to the right of grid may be used as bounds. You may also display parameters and sample size from your pilot study in this grid by inputting a vector (observedPara) of consensus values of the variance and the corresponding difference, or rbind several vectors of such parameters into a matrix/dataframe if you have multiple pilots.
这个函数绘制一个格计算样本规模的基础上的差异与分歧，重要的临床价值。从过去的癌症蛋白质分析研究的数据分析的基础上，我们临床上重要的参数定义为媒介生物学变异峰强度的汇总统计。在网格上，你可以从范围广泛，从过去的蛋白质组学分析研究现实生活中的数据显示参数值，包括：从早期和晚期大肠癌患者尿液和血清标本;大肠癌患者的血清样本数据四个SELDI技术的芯片类型（的IMAC，H50，Q10和CM10），从Limanda limanda鱼血浆样品检测大肠癌患者尿液样本分析使用SELDI技术和MALDI来样加工协议。这些值可能被用来作为选择样本大小的计算参数的指引。如果你的研究涉及到了晚期疾病或在IMAC芯片检测血清样品分析，则样本量可能是一个值接近外左侧轮廓。尿液分析研究需要更多的样本检测差异和价值的轮廓电网权利，可用于为界。您也可以从您在这个电网的试点研究显示，通过输入矢量（observedPara）的共识值的方差和相应的差额，或rbind成数等参数向量参数和样本大小矩阵/ dataframe如果你有多个飞行员。
用法----------Usage----------
sampleSizeContourPlots(Z,m,DIFF,VAR,beta,alpha,observedPara,Indicator)
参数----------Arguments----------
参数：Z
the heterogeneity correction factor.
异质性校正因子。
参数：m
the number of replicates.
复制的数量。
参数：DIFF
the clinically important difference.
临床上重要的差异。
参数：VAR
the protein variance.
蛋白质的变异。
参数：beta
the power to estimate the clinically important difference.
估计临床上重要的区别。
参数：alpha
the significance level.
显着性水平。
参数：observedPara
a vector or a matrix/dataframe (if there is more than one pilot study) containing the variance(s) and the clinically important difference(s) observed from your pilot data. The first element (column) of the vector (matrix) contains the observed variances, while the second contains the information on the clinically important difference(s).
向量或矩阵/ dataframe的（如果有一个以上的试验研究），其中包含的方差（S）和临床上重要的差异（S）观察试验数据。第一个元素（列）向量（矩阵）包含所观察到的差异，而第二个包含在临床上的重要区别（S）的信息。
参数：Indicator
an indicator variable. If it is set to 1, then the results of previous proteomic profiling studies together with the results of your pilot study are included in the plot. If it is set to 0, it leads to a plot of only the latter.
指示器变量。如果它被设置为1，那么以前的蛋白质组学分析研究与试验研究的结果，结果中的图。如果它被设置为0，它会导致只有后者的图。
值----------Value----------
Plots of grids of variance versus the clinically important differences with sample size contours superimposed on it.
图网格方差与样本规模上叠加轮廓的临床上重要的差异。
作者（S）----------Author(s)----------
Stephen Nyangoma
参考文献----------References----------
Billingham LJ: Sample size calculations for planning clinical proteomic profiling studies using mass spectrometry. Bioinformatics, 2009, Submitted
Billingham LJ: Issues in sample size calculations for designing cancer proteomic profiling studies.
举例----------Examples----------
# The plot will be saved in your working directory.[该图将被保存在你的工作目录。]
# On the grid, we have plotted a number of sample sizes we computed from real life data.[在网格上，我们绘制一些我们从现实生活中的数据计算的样本大小。]
#From these values you can gauge how many samples you may need.[从这些值，你可以衡量你可能需要多少样本。]
# Fewer samples than 50, will not result in any meaningful estimation of differences.[比50少的样本，将不会导致任何有意义的差异估计。]
# For late-stage cancer you need the fewest samples, even from a very variable sample such as urine.[对于晚期癌症，你需要最少即使从非常变量的样本，如尿液样本。]
# You need more samples, over 200, to estimate differences between early stage cancer and [你需要更多的样本，超过200个，估计早期癌症和之间的差异]
#noncancer controls.[非癌症控制。]
#etc.[等等]
m <- 2
DIFF <- seq(0.1,0.50,0.01) # 0.01[0.01]
VAR <- seq(0.2,4,0.1)
beta <- c(0.90,0.80,0.70)
alpha <- 1 - c(0.001, 0.01,0.05)/2
Corr <- c(0.70,0.90) #intraclass correlation also fixed[也是固定的组内相关]
Z <- 2.6 # fix at 2.6 or use FisherInformation(???)[定在2.6或使用FisherInformation（？？）]
# You may input parameters from your pilot study. Suppose they are: [你可以从你的试验研究输入参数。假设它们是：]
#observedPara=c(1,0.4) #the variance you computed from pilot data[observedPara = C（1,0.4）＃你从试验数据计算方差]
observedPara <- data.frame(var=c(0.7,0.5,1.5),DIFF=c(0.37,0.33,0.43))
# you may set these values to 0, if you do not have pilot data[你可以将这些值设置为0，如果你没有试验数据]
#observedVAR=0[observedVAR = 0]
#observedDIFF=0[observedDIFF = 0]
# in this case the values computed from my pilot studies (dotted on the plot) [在这种情况下，计算值从我的试验性研究（上点缀着图）]
# may be used as guidelines.[可作为指导方针。]
Indicator <- 0 #1[1]
sampleSizeContourPlots(Z,m,DIFF,VAR,beta,alpha,observedPara,Indicator)
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 2012-2-25 15:09:03
