speffSurv(speff2trial)
speffSurv()所属R语言包：speff2trial
Semiparametric efficient estimation and testing for a two-sample treatment effect with a right-censored time-to-event endpoint
半参数有效估计和检测两个样品的治疗效果与右删失时间对事件的终点
译者：生物统计家园网 机器人LoveR
描述----------Description----------
speffSurv conducts estimation and testing of the treatment effect in a two-group randomized clinical trial with a right-censored time-to-event endpoint. It improves efficiency by leveraging baseline predictors
speffSurv进行估计和检验的治疗效果在两个组随机临床试验的权利审查的时间对事件的端点。它提高了工作效率，充分利用基线预测
用法----------Usage----------
speffSurv(formula, data, force.in=NULL, nvmax=9,
method=c("exhaustive", "forward", "backward"),
optimal=c("cp", "bic", "rsq"), trt.id,
conf.level=0.95, fixed=FALSE)
参数----------Arguments----------
参数：formula
a formula object with the response variable on the left of the ~ operator and the linear predictor on the right. The response is a survival object of class Surv. The linear predictor specifies baseline variables that are considered for inclusion by the automated procedure for selecting the best models predicting the endpoint. Interactions and variable transformations might also be considered.
与响应变量~运营商和线性预测器在右侧上的左侧的一个公式对象。的反应是一种生存的对象的类Surv。的线性预测指定的自动程序选择最好的模型预测端点基线变量，考虑将其纳入。也可能被认为是相互作用和变量变换。
参数：data
a data frame in which to interpret the variables named in the formula and trt.id.
一个数据框中，解释变量命名为formula和trt.id。
参数：force.in
a vector of indices to columns of the design matrix that should be included in each regression model.
的矢量，应包括在每个回归模型的设计矩阵的列的索引。
参数：nvmax
the maximum number of covariates considered for inclusion in a model. The default is 9.
考虑纳入模型中的协变量的最大数量。默认值为9。
参数：method
specifies the type of search technique used in the model selection procedure carried out by the regsubsets function. "exhaustive" (default) performs the all-subsets selection, whereas "forward" and "backward" execute a forward or backward step-wise selection, respectively.
指定类型的搜索技术，用于在模型中选择程序进行了regsubsets功能。 “exhaustive”（默认）执行的所有子集的选择，而“forward”，“backward”执行向前或向后逐步选择，分别。
参数：optimal
specifies the optimization criterion for model selection. The default is "cp", Mallow's Cp, which is equivalent to AIC. The other options are "bic" for BIC and "rsq" for R-squared.
指定的优化模型选择准则。默认值是“cp”，Mallow的Cp，这是相当于AIC。其他选项“bic”BIC“rsq”R-平方。
参数：trt.id
a character string specifying the name of the treatment indicator which can be a character or a numeric vector. The control and treatment group is defined by the alphanumeric order of labels used in the treatment indicator.
治疗指示器，它可以是一个字符或一个数值向量指定的名称的字符串。的控制和治疗组的定义由字母数字顺序治疗指示器在使用的标签。
参数：conf.level
the confidence level to be used for confidence intervals reported by <br> summary.speffSurv.
置信水平被用于报告的参考summary.speffSurv的置信区间。
参数：fixed
logical value; if FALSE (default), automated selection procedure is used for predicting the endpoint. Otherwise, all baseline variables specified in the formula are used.
逻辑值; FALSE如果（默认），自动选择程序用于预测的端点。否则，使用式中的变量指定的所有基线。
Details
详细信息----------Details----------
The treatment effect is represented by the (unadjusted) log hazard ratio for the treatment versus control group. The estimate of the treatment effect using the (unadjusted) proportional hazards model is included in the output.
log（未经调整）的危险比用于治疗与对照组的治疗效果表示。 （未经调整）比例风险模型估计的治疗效果，包括在输出中。
Using the automated model selection procedure performed by regsubsets, two optimal linear regression models are developed to characterize the influence function of an estimator that is more efficient than the maximum partial likelihood estimator. The "efficient" influence function is searched in the space of influence functions that determine all regular and asymptotically linear estimators for the treatment effect (for definitions see, for example, Tsiatis, 2006). The space of influence functions has three components: the estimation space that characterizes all regular and asymptotically linear estimators that do not use baseline covariates. The other two subspaces, the randomization and censoring space, use baseline covariates to improve the efficiency in the estimation of the treatment effect (Lu, 2008). The automated model selection procedure is used to identify functions in the randomization and censoring space that satisfy a prespecified optimality criterion and that lead to efficiency gain by using baseline predictors of the outcome.
使用自动模式选择程序进行regsubsets，两个最优线性回归模型的估计是更有效的最大局部似然估计的影响函数的特征。中搜索空间的影响函数，确定所有常规和渐近线性估计的治疗效果（定义见，例如，Tsiatis，2006年）的“高效”的影响作用。影响功能的空间有三个组成部分：所有常规和的渐近线性估计，不使用基线协变量的估计空间的特点。另外两个子空间，随机化和审查的空间，使用基线协变量的估计的治疗效果，以提高效率（卢，2008年）。自动模式选择程序是用来识别功能的随机和审查的空间，满足预先设定的最优标准和提高效率，导致通过使用基线预测的结果，。
The user has the option to avoid the automated variable selection and, instead, use all variables specified in the formula for the estimation of the treatment effect. This is achieved by setting fixed=TRUE.
用户可以选择避免自动变量的选择，而不是使用所有变量中指定的公式估计的治疗效果。这是通过设置fixed=TRUE。
speffSurv does not allow missing values in the data.
speffSurv不允许丢失数据中的值。
值----------Value----------
speffSurv returns an object of class "speffSurv" which can be processed by <br> summary.speffSurv to obtain or print a summary of the results. An object of class "speffSurv" is a list containing the following components: <table summary="R valueblock"> <tr valign="top"><td>beta</td> <td> a numeric vector with estimates of the treatment effect from the unadjusted proportional hazards model and the semiparametric efficient model using baseline covariates, respectively.</td></tr> <tr valign="top"><td>varbeta</td> <td> a numeric vector of variance estimates for the treatment effect estimates in beta.</td></tr> <tr valign="top"><td>formula</td> <td> a list with components rndSpace and censSpace containing formula objects for the optimal selected linear regression models that characterize the optimal elements in the randomization and censoring space, respectively. Set to NULL if fixed=TRUE.</td></tr> <tr valign="top"><td>fixed</td> <td> a logical value; if TRUE, the efficient estimator utilizes all baseline covariates specified in the formula. Otherwise, the automated selection procedure is used to identify covariates that ensure optimality.</td></tr> <tr valign="top"><td>conf.level</td> <td> confidence level of the confidence intervals reported by summary.speffSurv.</td></tr> <tr valign="top"><td>method</td> <td> search technique employed in the model selection procedure.</td></tr> <tr valign="top"><td>n</td> <td> number of subjects in each treatment group.</td></tr> </table>
speffSurv返回一个类的对象“speffSurv”，它可以处理的参考summary.speffSurv，获得或打印结果的摘要。类的一个对象“speffSurv”是一个列表，其中包含以下组件：<table summary="R valueblock"> <tr valign="top"> <TD>beta </ TD> <TD一个数值向量的估计未经调整的比例风险模型和半参数有效的使用基线协变量的模型的治疗效果。</ TD> </ TR> <tr valign="top"> <TD>varbeta </ TD> <TD>一个数值向量的方差估计的治疗效果，估计在beta。</ TD> </ TR> <tr valign="top"> <TD> formula </ TD> <TD>组件的列表rndSpace和censSpace的最佳选择特征的线性回归模型中的随机和审查空间的最佳元素，分别包含公式对象。设置NULL如果fixed=TRUE。</ TD> </ TR> <tr valign="top"> <TD>fixed </ TD> <td>一个逻辑值; TRUE，有效的估计采用基线协变量公式中的规定。否则，自动选择程序，用来标识，以确保最优的协变量。</ TD> </ TR> <tr valign="top"> <TD>conf.level </ TD> <TD>信心水平报告的置信区间summary.speffSurv。</ TD> </ TR> <tr valign="top"> <TD>method</ TD> <TD>搜索技术模型中的选择过程。 </ TD> </ TR> <tr valign="top"> <TD>n </ TD> <TD>每个治疗组的受试者数。</ TD> </ TR> </表>
参考文献----------References----------
Lu X, Tsiatis AA. (2008), "Improving the efficiency of the log-rank test using auxiliary covariates.", Biometrika, 95:679–694.
Tsiatis AA. (2006), Semiparametric Theory and Missing Data., New York: Springer.
参见----------See Also----------
summary.speffSurv
summary.speffSurv
实例----------Examples----------
str(ACTG175)
data <- na.omit(ACTG175[ACTG175$arms==0 | ACTG175$arms==1,])
### efficiency-improved estimation of log hazard ratio using[＃＃效率提高log的危险比估计的使用]
### baseline covariates[＃＃基线协变量]
fit1 <- speffSurv(Surv(days,cens) ~ cd40+cd80+age+wtkg+drugs+karnof+z30+
preanti+symptom, data=data, trt.id="arms")
### 'fit2' coerces the use of all specified baseline covariates;[＃＃FIT2胁迫所有指定的基线协变量的使用;]
### automated selection procedure is skipped[＃＃自动选取程序将被跳过]
fit2 <- speffSurv(Surv(days,cens) ~ cd40+cd80+age+wtkg+drugs+karnof+z30+
preanti+symptom, data=data, trt.id="arms", fixed=TRUE)
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。
|
发表于 2012-9-30 14:57:03
