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

loveR · 发表于 2012-9-30 00:53:58

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

                                       principal stratification sensitivity analysis with time to event data relaxing monotonicity assumption.
                                       主要分层事件数据的时间来放松单调性假设的敏感性分析。

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

描述----------Description----------

Principal stratification sensitivity analysis with time to event data relaxing monotonicity as described by Shepherd, Gilbert, and Dupont (in press).
主要分层敏感性分析事件数据的时间来放松单调牧羊犬，吉尔伯特，杜邦公司（出版中）所描述的。

用法----------Usage----------

sensitivitySGD(z, s, d, y, v, beta0, beta1, phi, Pi, psi, tau,
            time.points, selection, trigger, groupings,
            followup.time,
            ci=0.95, ci.method = c("bootstrap", "analytic"),
            ci.type="twoSided", custom.FUN = NULL, na.rm = FALSE,
            N.boot = 100L, N.events = NULL, interval = c(-100, 100),
            upperTest = FALSE, lowerTest = FALSE, twoSidedTest=TRUE,
            inCore = TRUE,verbose = getOption("verbose"),
            colsPerFile = 1000L, 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：d
vector; indicates whether a post-selection event has occurred. Can be NA for unselected records.
向量;表示是否选择后的事件已经发生。可以NA未选中的记录。

参数：y
vector; the length of time from selection until event (d) or censoring. Can be NA for unselected records.
矢量选择，直到事件的时间长度（d）或审查。可以NA未选中的记录。

参数：v
numeric vector; the length of time from randomization until selection or censoring.
数字矢量的长度从随机的时间，直到选择或审查。

参数：beta0
numeric vector; values of the sensitivity parameter β linking outcome in group <VAR>g0</VAR> with selection if assigned group <VAR>g1</VAR>.
数字矢量值的敏感性参数β联组结果<VAR> G0 </ VAR>与selection，如果指定的组<VAR> G1 </ VAR>。

参数：beta1
numeric vector; values of the sensitivity parameter β linking outcome in group <VAR>g1</VAR> with selection if assigned group <VAR>g0</VAR>.
数字矢量值的敏感性参数β联组结果<VAR> G1 </ VAR>用selection如果指定的组<VAR> G0 </ VAR>。

参数：phi, Pi, psi
vectors; sensitivity parameters specifying the joint distribution of S(\var{g0}), S(\var{g1}).  Only one of the three parameters should be specified. psi is the log-odds ratio of selection. Pi is the probability of being in the always selected principal stratum (Pr(S(\var{g0}) = S(\var{g1}) = selected)). phi is the probability of selection in group <VAR>g0</VAR> given selection in group <VAR>g1</VAR> (Pr(S(\var{g0}) = 1|S(\var{g1}) = 1)).
向量灵敏度参数的联合分布S(\var{g0})，S(\var{g1})。只有一个的三个参数应该被指定。 psi的选择是对数的比值比。 Pi的概率是在总是选择主要的层（Pr(S(\var{g0}) = S(\var{g1}) = selected)）。 phi的概率是选择组<VAR> G0 </ VAR>组给定的选择<VAR> G1 </ VAR>（Pr(S(\var{g0}) = 1|S(\var{g1}) = 1)）。

参数：tau
maximum observed follow-up time after selection.  Selection weights are constant for \var{t}>\code{tau}.
最大随访时间后选择。为\var{t}>\code{tau}选择的权重是不变的。

参数：time.points
vector; time points, <VAR>t</VAR>, at which SCE(\var{t}) will be estimated.
向量的时间点，<VAR> </ VAR>，在SCE(\var{t})将估计。

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

参数：trigger
The value of d that denotes the post-selection event.
d表示事件后的选择。

参数：groupings
Vector of two elements c(<VAR>g0</VAR>,<VAR>g1</VAR>), the first element <VAR>g0</VAR> being the value of z the 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>)，第一个元素<VAR> G0 </ VAR>的价值z描绘了第一组的最后一个元素<VAR> G1 </ VAR>的值z描绘了第二组。

参数：followup.time
numeric value; cut-off point for v after which records are lost to censoring.
数值截止点v后的记录是输给了审查。

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”. Currently only works for “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”。目前只适用于“引导”。

参数：ci.type
character vector; type of confidence interval that the corresponding ci element is refering 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. Fas0, Fas1, time.points, p0, p1 are available to be used as arguments in the custom function.  The custom function must return a vector of elements that is the same length as time.points.
功能自定义的函数来计算结果。 Fas0，Fas1，time.points，p0，p1是被用来作为自定义函数的参数中。自定义函数必须返回一个向量的元素是相同长度的time.points。

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：N.events
integer; number of selection-events (S) for each bootstrap replication when doing selection-event based bootstrapping.
整数选择事件（S）为每个引导复制做选择时，基于事件的引导。

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

参数：lowerTest
logical.  Return the lower one sided p-value for SCE. Defaults to FALSE
逻辑。返回较低的片面的P-值SCE。默认为FALSE

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

参数：twoSidedTest
logical.  Return a two sided p-value for SCE. Defaults to TRUE
逻辑。返回的双面SCE p值。默认为TRUE

参数：verbose
logical;  prints dots when bootstrapping to show that something is happening.  Bootstrapping can take a long time.
逻辑打印点在引导时显示某些事情正在发生。自举可能需要很长的时间。

参数：inCore
logical; running in memory if TRUE, running with scratch files if FALSE.  Default is TRUE.  For large data analysis, the user may want to switch this to FALSE to allow for processing on data sets larger than can fit in memory.
运行逻辑; TRUE，运行临时文件在内存中，如果如果FALSE。默认是TRUE。对于大型数据分析，用户可能想切换到FALSE允许设置太大而不适合在内存中的数据进行处理。

参数：colsPerFile
integer; number of columns of the scratch file to process in each pass (e.g., 100 columns).
整数;数列的临时文件在每个通处理（例如，100列）。

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 “survival causal effect” among those who would have been selected regardless of treatment assignment (SCE) without assuming monotonicity (i.e., that one of the principal stratum is empty).  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 independent censoring (i.e., time from selection to event is independent of time from selection until censoring).  SCE is identified by assuming values for the sensitivity parameters beta0, beta1, and one of the parameters phi, psi, or Pi.  The sensitivity parameters beta0 and beta1 have a log-odds ratio interpretation (see help for sensitivityGBH).  Given selection in one treatment arm, the probability of selection if in the other treatment arm is assumed to be constant for for T(\code{z})>\code{tau}.
进行了敏感性分析，估计“生存因果关系”在那些谁被选中的治疗分配（SCE）没有假设单调性（也就是说，主要地层之一是空的）。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），和独立的审查（即，从选材到事件的时间是从选择的时间，直到独立审查）。 SCE假设的灵敏度参数的值被识别beta0，beta1，和的参数之一phi，psi或Pi。灵敏度参数beta0和beta1有一个数几率比解释（见sensitivityGBH）的帮助。在一个处理的臂，所述的选择概率，如果给定的选择中的其他治疗臂被假定为常数用于T(\code{z})>\code{tau}。

Only one of the parameters phi, psi, or Pi should be specified as all depend on each other.  psi is unrestrained taking any value on the real line.  The other parameters, phi and Pi have constraints and there will be estimation problems if these parameters are set at values outside the of their range of acceptable values based on the observed data.  See Shepherd, Gilbert, Dupont (in press) for more details.
只有一个参数phi，psi或Pi应指定为互相依赖的。 psi无拘无束的实线的任何值。其他参数，phi和Pi有限制，估计会有问题，如果这些参数设置为他们的观测数据的基础上可接受值的范围以外的值。有关详细信息，请参阅牧羊犬，吉尔伯特，杜邦（记者）。

值----------Value----------

object of class sensitivity3d
对象的类sensitivity3d

<table summary="R valueblock"> <tr valign="top"><td>SCE</td> <td>  array; Calculated values of SCE for all combinations of the values from beta0, beta1, phi/Pi/psi, and time.points.  Array dimensions are length(time.points), length(beta0), length(beta1), length(psi). </td></tr> <tr valign="top"><td>SCE.ci</td> <td>  array; Confidence interval of the SCE value.  Confidence interval determined by quantile if using ci.method “bootstrap”.  Otherwise calculated using analytic variance with large sample normal approximation. Array dimensions the same as element SCE. </td></tr> <tr valign="top"><td>SCE.var</td> <td>  array; estimated variance of SCE.  Array dimensions the same as element SCE. </td></tr> <tr valign="top"><td>beta0</td> <td>  vector; β values used for first group. </td></tr> <tr valign="top"><td>beta1</td> <td>  vector; β values used for second group. </td></tr> <tr valign="top"><td>psi</td> <td>  vector; ψ values used. </td></tr> <tr valign="top"><td>Pi</td> <td>  vector; Pi values used. </td></tr> <tr valign="top"><td>psi</td> <td>  vector; psi values used. </td></tr> <tr valign="top"><td>ci.map</td> <td>  list; mapping of confidence interval to quantile probability.  Use numbers contained within as indices to the SCE.ci element. </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD>SCE </ TD> <TD>阵列; SCE的值的所有组合的计算值从beta0 beta1，phi/Pi/psi，time.points。阵列的尺寸是length(time.points)，length(beta0)，length(beta1)，length(psi)。 </ TD> </ TR> <tr valign="top"> <TD>SCE.ci </ TD> <TD>阵列的SCE值的置信区间。置信区间由quantile，如果使用ci.method“引导”。否则使用分析方差大样本正常逼近的计算。数组维数相同元素SCE。 </ TD> </ TR> <tr valign="top"> <TD>SCE.var </ TD> <TD>阵列，估计方差SCE。数组维数相同元素SCE。 </ TD> </ TR> <tr valign="top"> <TD>beta0 </ TD> <TD>向量;β用于第一组的值。 </ TD> </ TR> <tr valign="top"> <TD>beta1 </ TD> <TD>向量;β用于第二组的值。 </ TD> </ TR> <tr valign="top"> <TD>psi </ TD> <TD> ψ向量;值。 </ TD> </ TR> <tr valign="top"> <TD>Pi </ TD> <TD> Pi向量;值。 </ TD> </ TR> <tr valign="top"> <TD>psi </ TD> <TD> psi向量;值。 </ TD> </ TR> <tr valign="top"> <TD>ci.map </ TD> <TD>名单;位数的概率的置信区间的映射。使用数字SCE.ci元素包含在作为指数。 </ 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----------

Shepherd BE, Gilbert PB, and Dupont CT, “Sensitivity analyses comparing time-to-event outcomes only existing in a subset selected postrandomization and relaxing monotonicity,” Biometrics, in press.

参见----------See Also----------

sensitivitySGL, sensitivityJR, Surv
sensitivitySGL，sensitivityJR，Surv

实例----------Examples----------

data(vaccine.trial)
sens.analysis<-with(vaccine.trial,
            sensitivitySGD(z=treatment, s=hiv.outcome, y=followup.yearsART,
                        d=ARTinitiation, beta0=c(0,-.25,-.5),
                        beta1=c(0, -.25, -.5), phi=c(0.95, 0.90), tau=3,
                        time.points=c(2,3), selection="infected",
                        trigger="initiated ART",
                        groupings=c("placebo","vaccine"), ci=.95,
                        ci.method="bootstrap", N.boot=100)
            )
sens.analysis

sens.analysis2<-with(vaccine.trial,
            sensitivitySGD(z=treatment, s=hiv.outcome, y=followup.yearsART,
                        d=ARTinitiation, beta0=c(0,-.25,-.5),
                        beta1=c(0, -.25, -.5), phi=c(0.95, 0.90), tau=3,
                        time.points=c(2,3), selection="infected",
                        trigger="initiated ART",
                        groupings=c("placebo","vaccine"), ci=.95,
                        custom.FUN=function(Fas0,Fas1,...,time.points) {
                        Fas0(time.points) - Fas1(time.points)
                        },
                        ci.method="bootstrap", N.boot=100)
            )
sens.analysis2

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

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