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

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

                                         principal stratification sensitivity analysis using the HHS method.
                                         主要分层敏感性分析使用HHS方法。

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Performs a principal stratification sensitivity analysis using the method described in Hudgens, Hoering, and Self (2003).
执行使用在哈金斯的方法，Hoering的主要分层灵敏度分析，并自（2003年）。


用法----------Usage----------


sensitivityHHS(z, s, y, bound = c("upper", "lower"), selection,
               groupings, empty.principal.stratum, ci = 0.95,
               ci.method = c("bootstrap", "analytic"),
               ci.type = "twoSided", custom.FUN = NULL, na.rm = FALSE,
               N.boot = 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 values. Can be NA for unselected records.
向量;结果值。可以NA未选中的记录。


参数：bound
vector; which bound should be calculated, “upper” and/or “lower”.  Partial string matching is performed.
应计算向量;绑定，“上”和/或“下”。部分字符串匹配。


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 to possible group values. The first element <VAR>g0</VAR> being the value of z which delineates the first group, the last element <VAR>g1</VAR> being the value of z which delineates the second group.
向量的两个元素c(<VAR>g0</VAR>,<VAR>g1</VAR>);描述可能的组值。第一元<VAR> G0 </ VAR>z描绘了第一组的最后一个元素<VAR> G1 </ VAR>值z的界定了第二的价值组。


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 &ldquo;analytic&rdquo; or &ldquo;bootstrap&rdquo;. Defaults to c("analytic","bootstrap"). Currently only works for &ldquo;bootstrap&rdquo;.
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")的。目前只适用于“引导”。


参数：ci.type
character vector; type of confidence interval that the corisponding ci element is referring to.  Can be &ldquo;upper&rdquo;, &ldquo;lower&rdquo;, or &ldquo;twoSided&rdquo;.  Defaults to "twoSided".     
字符向量的置信区间，corisponding ci元素是指类型。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 &ldquo;bootstrap&rdquo;.
整数。将运行时ci.method包括“引导”，引导重复的号码。


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 &ldquo;ACE&rdquo;, &ldquo;T1&rdquo;, or &ldquo;T2&rdquo;. Defaults to "ACE".
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。


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 still not identified after making these assumptions, so this method computes the lower and upper bounds of the estimated ACE.  These bounds correspond to the values one would get if using sensitivityGBH and specifying the sensitivity parameter beta as -Inf or Inf.
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后仍然没有确定这些假设，因此这种方法计算出的上限和下限的估计ACE。这些边界对应的值，如果使用sensitivityGBH和-Inf或Inf指定的敏感性参数测试。


值----------Value----------

an object of class sensitivity2d.
对象类sensitivity2d。

<table summary="R valueblock"> <tr valign="top"><td>ACE</td> <td>  ACE=E(Y(\var{g1})-Y(\var{g0})|S(\var{g1})=S(\var{g0})=\code{selection}). Vector of the estimated ACE values at the specified bounds. Only exists if method includes &ldquo;ACE&rdquo;. </td></tr> <tr valign="top"><td>ACE.ci</td> <td>  vector; confidence interval of ACE determined by quantiles of bootstrap if ci.method includes &ldquo;bootstrap&rdquo;.  Otherwise calculated using analytic variance with large sample normal approximation (NOT YET WORKING). Only exists if method includes &ldquo;ACE&rdquo;.  </td></tr> <tr valign="top"><td>ACE.var</td> <td>  vector; estimated variance of ACE. Only exists if method includes &ldquo;ACE&rdquo;. </td></tr> <tr valign="top"><td>ACE.p</td> <td>  vector; estimated p-value of ACE.  Only exists if method includes &ldquo;ACE&rdquo;. </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 at the bounds.        Pr(Y(\var{g0}) <= \var{y0}|S(\var{g0}) = S(\var{g1}) = \code{selection}) </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 at the bounds.        Pr(Y(\var{g1}) <= \var{y1}|S(\var{g0}) = S(\var{g1}) = \code{selection}) </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> ACE</ TD> <TD>ACE=E(Y(\var{g1})-Y(\var{g0})|S(\var{g1})=S(\var{g0})=\code{selection})。向量的估计的ACE值在指定的范围。只存在如果method包含“ACE”。 </ TD> </ TR> <tr valign="top"> <TD>ACE.ci </ TD> <TD>向量的置信区间确定的ACE位数的引导，如果ci.method＆ldquo引导“。否则，计算分析方差与大样本正常近似（没有工作）。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>Fas0 </ TD> </ VAR <TD>功能;估计的经验分布函数值<VAR> Y0>第一组总是选择主要地层的界限。        Pr(Y(\var{g0}) <= \var{y0}|S(\var{g0}) = S(\var{g1}) = \code{selection})</ 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主要地层的界限。        Pr(Y(\var{g1}) <= \var{y1}|S(\var{g0}) = S(\var{g1}) = \code{selection})</ 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----------

Hudgens MG, Hoering A, and Self SG (2003), &ldquo;On the Analysis of Viral Load Endpoints in HIV Vaccine Trials,&rdquo; Statistics in Medicine 22, 2281-2298.

参见----------See Also----------

sensitivityGBH, sensitivityJR, sensitivitySGL
sensitivityGBH，sensitivityJR，sensitivitySGL


实例----------Examples----------


data(vaccine.trial)
est.bounds<-with(vaccine.trial,
                 sensitivityHHS(z=treatment, s=hiv.outcome, y=logVL,
                     selection="infected", groupings=c("placebo","vaccine"),
                     empty.principal.stratum=c("not infected","infected"),
                     N.boot=100)
                )
est.bounds

est.bounds<-with(vaccine.trial,
                 sensitivityHHS(z=treatment, s=hiv.outcome, y=logVL,
                     selection="infected", groupings=c("placebo","vaccine"),
                     empty.principal.stratum=c("not infected","infected"),
                     method=c("ACE", "T1", "T2"), N.boot=100,
                     custom.FUN=function(mu0, mu1, ...) mu1 - mu0,
                     upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE)
                )
est.bounds


转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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