R语言 sensitivityPStrat包 sensitivityGBH()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-30 00:53:32

sensitivityGBH(sensitivityPStrat)
sensitivityGBH()所属R语言包：sensitivityPStrat

                                       Principal stratification sensitivity analysis.
                                       主要分层的敏感性分析。

                                       译者：生物统计家园网机器人LoveR

描述----------Description----------

Performs a sensitivity analysis using the method described in Gilbert, Bosch, and Hudgens (2003).
执行敏感性分析用，在吉尔伯特，博世，和哈金斯（2003）描述的方法。

用法----------Usage----------

sensitivityGBH(z, s, y, beta, selection, groupings,
            empty.principal.stratum, ci = 0.95,
            ci.method = c("analytic", "bootstrap"),
            ci.type = "twoSided", custom.FUN = NULL, na.rm = FALSE,
            N.boot = 100, interval = c(-100, 100),
            upperTest = FALSE, lowerTest = FALSE, twoSidedTest = TRUE,
            method = c("ACE", "T1", "T2"), isSlaveMode=FALSE)

参数----------Arguments----------

参数：z
vector; contains the grouping values (e.g., treatment assignment) for each record.
矢量包含分组的每条记录的值（例如，治疗分配）。

参数：s
vector; indicates whether a record is selected.
矢量表示一个记录是否被选中。

参数：y
vector; outcome value. Can be NA for unselected records.
矢量的结果值。可以NA未选中的记录。

参数：beta
vector; values of the β sensitivity parameter.  Inf and -Inf are acceptable.
向量; β灵敏度参数的值。 Inf和-Inf是可以接受的。

参数：selection
The value of s indicating selection.
的值s表示选择。

参数：groupings
vector of two elements c(<VAR>g0</VAR>,<VAR>g1</VAR>); describes the possible group values. The first element <VAR>g0</VAR> being the value of z that delineates the first group, the last element <VAR>g1</VAR> being the value of z that delineates the second group.
向量的两个元素c(<VAR>g0</VAR>,<VAR>g1</VAR>);描述了可能的组值。第一元<VAR> G0 </ VAR>的价值z描绘了第一组的最后一个元素<VAR> G1 </ VAR>值的z，勾画出第二组。

参数：empty.principal.stratum
vector of two elements c(<VAR>s0</VAR>,<VAR>s1</VAR>); describes the s values that select the empty principal stratum.  If empty.principal.stratum=c(<VAR>s0</VAR>,<VAR>s1</VAR>), then stratum defined by S(\var{g0}) = \var{s0} and S(\var{g1}) = \var{s1} is the empty stratum.  In this example <VAR>s0</VAR> and <VAR>s1</VAR> refer to the two possible values of s. (Note: method only works if \var{s0} != \var{s1}).
向量的两个元素c(<VAR>s0</VAR>,<VAR>s1</VAR>);介绍了s的值选择空的主要阶层。如果empty.principal.stratum=c(<VAR>s0</VAR>,<VAR>s1</VAR>)，然后阶层定义的S(\var{g0}) = \var{s0}和S(\var{g1}) = \var{s1}是空的阶层。在这个例子<VAR> S0 </ VAR>和<VAR> S1 </ VAR>的两个可能值s。（注：方法只适用于如果\var{s0} != \var{s1}）。

参数：ci
numeric vector; confidence interval level. Defaults to 0.95
数值向量的置信区间水平。默认为0.95

参数：ci.method
character; method by which the confidence interval and variance are calculated.  Can be “analytic” or “bootstrap”. Defaults to c("analytic","bootstrap")
字符;方法，通过该方法计算的置信区间和方差。可以“分析”或“引导”。默认为c("analytic","bootstrap")

参数：ci.type
character vector; type of confidence interval that the corresponding ci element is referring to.  Can be “upper”, “lower”, or “twoSided”.  Defaults to "twoSided".
字符向量的置信区间相应的ci元素指的类型。可以是“上”，“下”，或“twoSided”。默认为"twoSided"的。

参数：custom.FUN
function; function to calculate custom result. mu0, mu1, p0, p1 are available to be used as arguments in the custom function, where \code{mu0} = E(Y(\var{g0})|S(\var{g0}) = S(\var{g1}) = selected), \code{mu1} = E(Y(\var{g1})|S(\var{g0}) = S(\var{g1}) = selected), \code{p0} = P(S(\var{g0}) = selected), and \code{p1} = P(S(\var{g1}) = selected). The custom function must return a single value.
功能自定义的函数来计算结果。 mu0，mu1，p0，p1可被用来作为自定义函数的参数，其中\code{mu0} = E(Y(\var{g0})|S(\var{g0}) = S(\var{g1}) = selected)，\code{mu1} = E(Y(\var{g1})|S(\var{g0}) = S(\var{g1}) = selected)，\code{p0} = P(S(\var{g0}) = selected)和\code{p1} = P(S(\var{g1}) = selected)。自定义函数必须返回一个值。

参数：na.rm
logical; indicates whether records that are invalid due to NA values should be removed from the data set.
逻辑表示是无效的，由于NA值的记录是否应该从数据集。

参数：N.boot
integer; number of bootstrap repetitions that will be run when ci.method includes “bootstrap”.
整数，将运行时ci.method包括“引导”，引导重复数目。

参数：interval
numeric vector of length 2. Controls the range limits used by optimize to estimate α.
数字矢量长度为2。控制范围限制optimize估计α。

参数：lowerTest
logical.  Return the lower one sided p-value for returned tests. Defaults to FALSE
逻辑。返回较低片面的P-值返回的测试。默认为FALSE

参数：upperTest
logical.  Return the upper one sided p-value for returned tests. Defaults to FALSE
逻辑。返回上一个双面P值返回测试。默认为FALSE

参数：twoSidedTest
logical.  Return a two sided p-value for returned tests. Defaults to TRUE
逻辑。返回的双面p值返回测试。默认为TRUE

参数：method
character vector; type of test statistic calculated. Can be one or more of “ACE”, “T1”, or “T2”. Defaults to "ACE".  Methods “T1” and “T2” are not implemented if ci.method includes “analytic”.
字符向量检验统计量的计算类型。可以是一个或更多的“ACE”，“T1”，“T2”。默认为"ACE"的。方法“T1”和“T2”不落实ci.method如果包括“分析”。

参数：isSlaveMode
logical.  Internal Use only. Used in recursion.
逻辑。仅供内部使用。在递归使用。

Details

详细信息----------Details----------

Performs a sensitivity analysis estimating the average causal effect among those who would have been selected regardless of treatment assignment (ACE).  The method assumes no interference (i.e., potential outcomes of all subjects are unaffected by treatment assignment of other subjects), ignorable (i.e., random) treatment assignment, and monotonicity (i.e., one of the principal strata is empty).  ACE is identified by assuming a value of the sensitivity parameter beta, where exp(β) has an odds ratio interpretation:
估计那些人会被选中，无论治疗分配（ACE）的平均之间的因果关系进行了敏感性分析。该方法假定无干扰（即，所有科目的可能的结果是处理其他科目分配的影响），可忽略处理分配（即随机），和单调性（即，其中的主要阶层是空的）。 ACE是确定假设的敏感性参数测试值，其中exp(β)有一个比值比解释：

If \code{empty.principal.stratum}=c(S(\var{g0})=not\ selected, S(\var{g1})=selected) then given selected if assigned <VAR>g0</VAR>, the odds of being selected if assigned <VAR>g1</VAR> multiplicatively increase exp(β) for every 1-unit increase in Y(\var{g0}).
如果\code{empty.principal.stratum}=c(S(\var{g0})=not\ selected, S(\var{g1})=selected)然后选择分配<VAR> G0 </ VAR>，被选中的可能性，如果分配<VAR> G1 </ VAR>成倍地增加exp(β)每增加1个单位，在Y(\var{g0})。

If \code{empty.principal.stratum}=c(S(\var{g0})=selected, S(\var{g1})=not\ selected) then given selected if assigned <VAR>g1</VAR>, the odds of being selected if assigned <VAR>g0</VAR> multiplicatively increase exp(β) for every 1-unit increase in Y(\var{g1}).
如果\code{empty.principal.stratum}=c(S(\var{g0})=selected, S(\var{g1})=not\ selected)然后选择分配<VAR> G1 </ VAR>的几率被选中，如果分配<VAR> G0 </ VAR>成倍地增加exp(β)每增加1个单位，在 Y(\var{g1})。

Specifying beta=-Inf or beta=Inf calls sensitivityHHS.
指定beta=-Inf或beta=InfsensitivityHHS。

T1 and T2 are rank-based analogs of ACE. See <REF TBD>.
T1和T2是秩为基础的类似物的ACE。请参见<ref TBD>。

值----------Value----------

an object of class sensitivity2d.
对象类sensitivity2d。

<table summary="R valueblock"> <tr valign="top"><td>ACE</td> <td>  vector; ACE = E(Y(\var{g1}) - Y(\var{g0})|S(\var{g1}) = S(\var{g0}) = \code{selection}). Vector of the estimated ACE values for specified beta values. Only exists if method includes “ACE”. </td></tr> <tr valign="top"><td>ACE.ci</td> <td>  array; confidence interval of ACE determined by quantiles of bootstrap if ci.method includes “bootstrap”.  Otherwise calculated using analytic variance with large sample normal approximation. Only exists if method includes “ACE”. </td></tr> <tr valign="top"><td>ACE.var</td> <td>  vector; estimated variance of ACE.  Only exists if method includes “ACE”. </td></tr> <tr valign="top"><td>ACE.p</td> <td>  vector; estimated p-value of ACE.  Only exists if method includes “ACE”. </td></tr> <tr valign="top"><td>T1</td> <td>  vector; Vector of the estimated T1 test statistic for specified beta values. Only exists if method includes “T1”. </td></tr> <tr valign="top"><td>T1.p</td> <td>  vector; estimated p-value of T1.  Only exists if method includes “T1”. </td></tr> <tr valign="top"><td>T2</td> <td>  vector; Vector of the estimated T2 statistic for specified beta values. Only exists if method includes “T2”. </td></tr> <tr valign="top"><td>T2.p</td> <td>  vector; estimated p-value of T2.  Only exists if method includes “T2”. </td></tr> <tr valign="top"><td>beta</td> <td>  vector; user-specified β values </td></tr> <tr valign="top"><td>alphahat</td> <td>  vector; estimated values of α </td></tr> <tr valign="top"><td>Fas0</td> <td>  function; estimator for the empirical distribution function values for <VAR>y0</VAR> in the first group in the always selected principal stratum. Pr(Y(\var{g0}) <= \var{y0}|S(\var{g1}) = \code{selection}; β) </td></tr> <tr valign="top"><td>Fas1</td> <td>  function; estimator for the empirical distribution function values for <VAR>y1</VAR> in the second group in the always selected principal stratum.  Pr(Y(\var{g1}) <= \var{y1}|S(\var{g0}) = S(\var{g1}) = \code{selection}; β) </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> ACE</ TD> <TD>矢量，“ACE = E(Y(\var{g1}) - Y(\var{g0})|S(\var{g1}) = S(\var{g0}) = \code{selection})。向量的估计ACE值指定的beta值。只存在如果method包含“ACE”。 </ TD> </ TR> <tr valign="top"> <TD> ACE.ci </ TD> <TD>阵列的置信区间确定的ACE位数的引导，如果ci.method＆ldquo引导“。否则使用分析方差大样本正常逼近的计算。只存在如果method包含“ACE”。 </ TD> </ TR> <tr valign="top"> <TD>ACE.var</ TD> <TD>向量;估计方差的ACE。只存在如果method包含“ACE”。 </ TD> </ TR> <tr valign="top"> <TD> ACE.p </ TD> <TD>矢量估计p值的ACE。只存在如果method包含“ACE”。 </ TD> </ TR> <tr valign="top"> <TD>T1 </ TD> <TD>向量，向量的估计T1指定了beta值的检验统计量。只存在如果method包含“T1”。 </ TD> </ TR> <tr valign="top"> <TD> T1.p </ TD> <TD>矢量估计的p值T1。只存在如果method包含“T1”。 </ TD> </ TR> <tr valign="top"> <TD>T2 </ TD> <TD>矢量矢量指定了beta值的估计T2统计。只存在如果method包含“T2”。 </ TD> </ TR> <tr valign="top"> <TD>T2.p </ TD> <TD>向量;估计p值的T2。只存在如果method包含“T2”。 </ TD> </ TR> <tr valign="top"> <TD>beta </ TD> <TD>向量;用户指定的β值</ TD> </ TR> <tr valign="top"> <TD> alphahat </ TD> <TD>矢量估计值α</ TD> </ TR> <tr valign="top"> < Fas0 TD> </ TD> <TD>功能;估计的经验分布函数值<VAR> Y0 </ VAR>在第一组总是选择主要地层。 Pr(Y(\var{g0}) <= \var{y0}|S(\var{g1}) = \code{selection}; β)</ TD> </ TR> <tr valign="top"> <TD>Fas1 </ TD> <TD>功能;估计的经验分布函数值<VAR> Y1 </ VAR>在第二组中总是选择的主要阶层。 Pr(Y(\var{g1}) <= \var{y1}|S(\var{g0}) = S(\var{g1}) = \code{selection}; β)</ TD> </ TR> </ TABLE>

（作者）----------Author(s)----------

Bryan E. Shepherd <br>
Department of Biostatistics<br>
Vanderbilt University<br>

Charles Dupont <br>
Department of Biostatistics<br>
Vanderbilt University<br>

参考文献----------References----------

Gilbert PB, Bosch RJ, and Hudgens MG (2003), “Sensitivity Analysis for the Assessment of Causal Vaccine Effects of Viral Load in HIV Vaccine Trials,” Biometrics 59, 531-541.

参见----------See Also----------

sensitivityHHS, sensitivityJR, sensitivitySGL
sensitivityHHS，sensitivityJR，sensitivitySGL

实例----------Examples----------

data(vaccine.trial)
ans<-with(vaccine.trial,
      sensitivityGBH(z=treatment,s=hiv.outcome,y=logVL,
                  beta=c(0,.25,.5,.75,1,1.25,1.5),
                  selection="infected",
                  groupings=c("placebo","vaccine"),
                  empty.principal.stratum=c("not infected","infected"),
                  N.boot=100)
      )
ans

ans<-with(vaccine.trial,
      sensitivityGBH(z=treatment,s=hiv.outcome,y=logVL,
                  beta=c(-Inf,-1,-0.75,-0.5,-0.25,0,.25,.5,.75,1,Inf),
                  selection="infected",
                  groupings=c("placebo","vaccine"),
                  empty.principal.stratum=c("not infected","infected"),
                  ci.method="bootstrap", ci=c(0.95, 0.9, 0.9),
                  ci.type=c('twoSided', 'upper', 'lower'),
                  custom.FUN=function(mu0, mu1, ...) mu1 - mu0,
                  N.boot=100, method=c("ACE", "T1", "T2"),
                  upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE)
      )
ans

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。

账号		自动登录	找回密码
密码			注册