speff(speff2trial)
speff()所属R语言包：speff2trial
Semiparametric efficient estimation and testing for a two-sample treatment effect with a quantitative or dichotomous endpoint
半参数有效的估计和检验治疗效果的定量或二分的端点的两个样本
译者：生物统计家园网 机器人LoveR
描述----------Description----------
speff conducts estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative or dichotomous endpoint. The method is a special case of Robins, Rotnitzky, and Zhao (1994, JASA). It improves efficiency by leveraging baseline predictors of the endpoint. The method uses
speff进行的估计和检验治疗效果，2组随机临床试验的定量或二分的端点。该方法是一种特殊的情况下，罗宾斯，Rotnitzky，赵（1994年，JASA）。提高效率，通过利用基线预测的端点。该方法使用
用法----------Usage----------
speff(formula, endpoint=c("quantitative", "dichotomous"), data,
postrandom=NULL, force.in=NULL, nvmax=9,
method=c("exhaustive", "forward", "backward"),
optimal=c("cp", "bic", "rsq"), trt.id, conf.level=0.95,
missCtrl=NULL, missTreat=NULL, endCtrlPre=NULL,
endTreatPre=NULL, endCtrlPost=NULL, endTreatPost=NULL)
参数----------Arguments----------
参数：formula
a formula object with the response on the left of the ~ operator, and the linear predictor on the right. The linear predictor specifies baseline and postrandomization variables that are considered for inclusion by the automated procedure for selecting the best models predicting the endpoint, separately for each treatment group. Interactions and variable transformations might also be considered. If predicted values for the endpoint are entered explicitly by the user, the formula can be of the form response ~ 1.
一个公式对象与~算子，和在右边的线性预测的左侧上的响应。线性预测指定基线和随机化后的变量，考虑将其纳入自动化程序选择最好的模型预测端点，每个治疗组分别为。也可能被认为是相互作用和变量变换。如果为端点的预测值由用户显式输入，公式的形式response ~ 1。
参数：endpoint
a character string specifying the type of the response variable. The option "quantitative" (default) classifies the response as quantitative, and the mean difference is the measure of the treatment effect, whereas "dichotomous" specifies a dichotomous response, and the log odds ratio is the measure of the treatment effect. Only the first character is necessary.
一个字符的字符串指定的类型的响应变量。选项“quantitative”（默认值）的反应进行分类定量的平均差异是衡量治疗效果，而“dichotomous”指定的二分法响应，并且log的比值比是测量的治疗效果。只有第一个字符是必要的。
参数：data
a data frame in which to interpret the variables named in the formula, <br> postrandom, and trt.id.
解释变量在一个数据框中的formula，参考postrandom，trt.id。
参数：postrandom
a character vector designating postrandomization covariates included in the formula (this argument allows to distinguish baseline from postrandomiation covariates).
字符向量指定随机化后的变量包括在公式中（该参数可以区分postrandomiation协变量的基线）。
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.speff.
置信水平被用于报告的参考summary.speff的置信区间。
参数：missCtrl
estimated probabilities of observing the endpoint based on pre- and postrandomization information in the control group
估计概率的基础上观察端点前和随机化后的对照组中的信息
参数：missTreat
estimated probabilities of observing the endpoint based on pre- and postrandomization information in the treatment group
在治疗组中的前和随机化后的信息估计概率的基础上观察端点
参数：endCtrlPre
predicted values of the endpoint using baseline information in the control group only
预测值的端点只使用在对照组的基线资料
参数：endTreatPre
predicted values of the endpoint using baseline information in the treatment group only
预测值的端点只使用在治疗组的基线资料
参数：endCtrlPost
predicted values of the endpoint using baseline and postrandomization information in the control group
在对照组中使用基线和随机化后的信息的预测值的端点
参数：endTreatPost
predicted values of the endpoint using baseline and postrandomization information in the treatment group
预测值的端点使用的基线和治疗组随机化后的信息
Details
详细信息----------Details----------
The treatment effect is represented by the mean difference or the log odds ratio for a quantitative or dichotomous endpoint, respectively. Estimates of the treatment effect that ignore baseline covariates (naive) are included in the output.
平均差或log的比值比为定量或二分法端点，分别表示的治疗效果。估计的治疗效果，忽略基线协变量（天真的）都包含在输出中。
Using the automated model selection procedure performed by regsubsets, four optimal regression models are developed for the study endpoint. Initially, all baseline and postrandomization covariates specified in the formula are considered for inclusion by the model selection procedure carried out separately in each treatment group. The optimal models are used to construct predicted values of the endpoint. Subsequently, in each treatment group, another regression model is fitted that includes only baseline covariates that were selected in the previous optimization. Then predicted values of the endpoint are computed based on these models. If missingness occurs in the endpoint variable, the model selection procedure is additionally used to determine the optimal models for predicting whether a subject has an observed endpoint, separately in each treatment group.
使用自动模式选择程序执行的regsubsets，4个最优回归模型的开发研究终点。最初，在公式中指定的所有基线和随机化后的协变量考虑纳入各治疗组分别进行模型的选择过程。优化模型被用来构建的预测值的端点。随后，每个治疗组中，嵌合另一个回归模型，仅包括基线协变量，在前面的优化选择。基于这些模型，然后预测值计算的端点。如果missingness发生在端点变量，模型选择程序另外使用，以确定最佳的模型预测受试者是否有一个观察到的端点，每个治疗组中分开。
The function regsubsets conducts optimization of linear regression models only. The following modification in the model selection is adopted for a dichotomous variable: initially, a logistic regression model is fitted with all baseline and postrandomization covariates included in the formula. Subsequently, an optimal model is selected by using a weighted linear regression with weights from the last iteration of the IWLS algorithm. The optimal model is then refitted by logistic regression.
regsubsets的功能进行优化的线性回归模型。以下修改：在模型的选择采用一个二分变量：最初，logistic回归模型拟合公式中的变量包括所有基线和随机化后。随后，选择的最佳模式通过使用与从最后一次迭代的IWLS算法的权重的加权线性回归。优化模型，然后装复用logistic回归。
Besides using the built-in model selection algorithms, the user has the option to explicitly enter predicted values of the endpoint as well as estimated probabilities of observing the endpoint if it is missing at random.
除此之外，使用内置在模型选择算法中，用户有明确输入预测的端点值，以及如果它缺少随机观察端点估计概率的选项。
值----------Value----------
speff returns an object of class "speff" which can be processed by summary.speff to obtain or print a summary of the results. An object of class "speff" is a list containing the following components: <table summary="R valueblock"> <tr valign="top"><td>coef</td> <td> a matrix with estimates of treatment-specific mean responses and the treatment effect.</td></tr> <tr valign="top"><td>cov</td> <td> a list with components naive and speff, each storing the covariance matrix of the estimated treatment-specific mean responses.</td></tr> <tr valign="top"><td>varbeta</td> <td> a numeric vector of variance estimates of the naive and semiparametric treatment effect estimates.</td></tr> <tr valign="top"><td>formula</td> <td> a list with components control and treatment containing formula objects for the optimal selected regression models. Set to NULL if predicted values are entered explicitly.</td></tr> <tr valign="top"><td>rsq</td> <td> a numeric vector of the R-squared statistics for the optimal selected regression models predicting the study endpoint. Set to NULL if predicted values are entered by the user.</td></tr> <tr valign="top"><td>endpoint</td> <td> "quantitative" for a quantitative and "dichotomous" for a dichotomous response.</td></tr> <tr valign="top"><td>postrandom</td> <td> a character vector of postrandomization covariates considered for selection.</td></tr> <tr valign="top"><td>predicted</td> <td> a logical vector; if TRUE, the built-in model selection procedure was employed for prediction of the study endpoint in the control and treatment group, respectively.</td></tr> <tr valign="top"><td>conf.level</td> <td> confidence level of the confidence intervals reported by summary.speff.</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>
speff返回一个类的对象“speff”，它可以处理summary.speff获得或打印结果的摘要。类的一个对象“speff”是一个列表，其中包含以下组件：<table summary="R valueblock"> <tr valign="top"> <TD>coef </ TD> <TD矩阵估计治疗的具体含义是什么反应和治疗效果。</ TD> </ TR> <tr valign="top"> <TD>cov </ TD> <td>一个列表组件naive和speff，每个存储的协方差矩阵的估计治疗的具体含义是什么反应。</ TD> </ TR> <tr valign="top"> <TD>varbeta </ TD> <td>一个数字向量，方差估计的天真和半参数治疗效果的估计。</ TD> </ TR> <tr valign="top"> <TD>formula</ TD > <td>一个组件列表control和treatment的最佳选择回归模型的公式对象。设置NULL，如果预测值显式输入。</ TD> </ TR> <tr valign="top"> <TD>rsq </ TD> <td>一个数字矢量的R-平方的最佳选择回归模型预测研究终点的统计数据。设置为NULL，如果用户输入的预测值。</ TD> </ TR> <tr valign="top"> <TD> endpoint</ TD> <TD>“ X>“的定量和”quantitative“的二分法响应。</ TD> </ TR> <tr valign="top"> <TD>dichotomous </ TD> <TD字符的随机化后的矢量协变量考虑的选择。如果<X </ TD> </ TR> <tr valign="top"> <TD>postrandom </ TD> <td>一个逻辑向量; >，内置预测研究终点的控制和治疗组，分别采用模型的选择过程。</ TD> </ TR> <tr valign="top"> <TD>predicted </ TD> <TD>所报告的置信区间的置信水平TRUE。</ TD> </ TR> <tr valign="top"> <TD> conf.level</ TD> <TD>搜索技术模型中的选择过程。</ TD> </ TR> <tr valign="top"> <TD>summary.speff </ TD> <TD>每个治疗组的受试者数</ TD> </ TR> </ TABLE>
参考文献----------References----------
Robins JM, Rotnitzky A, Zhao LP. (1994), "Estimation of regression coefficients when some regressors are not always observed.", Journal of the American Statistical Association, 89:846–66.
Tsiatis AA, Davidian M, Zhang M, Lu X. (2007), "Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach.", Statistics in Medicine, 27:4658–4677.
Zhang M, Tsiatis AA, Davidian M. (2008), "Improving efficiency of inferences in randomized clinical trials using auxiliary covariates.", Biometrics, 64:707–715.
Davidian M, Tsiatis AA, Leon S. (2005), "Semiparametric estimation of treatment effect in a pretest-posttest study with missing data.", Statistical Science, 20:261–301.
Zhang M, Gilbert P. (2009), "Increasing the efficiency of prevential trials by incorporating baseline covariates.", manuscript.
参见----------See Also----------
summary.speff
summary.speff
实例----------Examples----------
str(ACTG175)
### treatment effect estimation with a quantitative endpoint missing[＃＃治疗效果的估计与定量端点失踪]
### at random[＃＃随机]
fit1 <- speff(cd496 ~ age+wtkg+hemo+homo+drugs+karnof+oprior+preanti+
race+gender+str2+strat+symptom+cd40+cd420+cd80+cd820+offtrt,
postrandom=c("cd420","cd820","offtrt"), data=ACTG175, trt.id="treat")
### 'fit2' adds quadratic effects of CD420 and CD820 and their [＃＃FIT2，增加了二次效应的CD420和CD820和]
### two-way interaction[＃＃双向互动]
fit2 <- speff(cd496 ~ age+wtkg+hemo+homo+drugs+karnof+oprior+preanti+
race+gender+str2+strat+symptom+cd40+cd420+I(cd420^2)+cd80+cd820+
I(cd820^2)+cd420:cd820+offtrt, postrandom=c("cd420","I(cd420^2)",
"cd820","I(cd820^2)","cd420:cd820","offtrt"), data=ACTG175,
trt.id="treat")
### 'fit3' uses R-squared as the optimization criterion[＃＃FIT3使用R-平方作为优化准则]
fit3 <- speff(cd496 ~ age+wtkg+hemo+homo+drugs+karnof+oprior+preanti+
race+gender+str2+strat+symptom+cd40+cd420+cd80+cd820+offtrt,
postrandom=c("cd420","cd820","offtrt"), data=ACTG175, trt.id="treat",
optimal="rsq")
### a dichotomous response is created with missing values maintained[＃＃二分响应创建的缺失值保持]
ACTG175$cd496bin <- ifelse(ACTG175$cd496 > 250, 1, 0)
### treatment effect estimation with a dichotomous endpoint missing[＃＃治疗效果估计二分端点失踪]
### at random[＃＃随机]
fit4 <- speff(cd496bin ~ age+wtkg+hemo+homo+drugs+karnof+oprior+preanti+
race+gender+str2+strat+symptom+cd40+cd420+cd80+cd820+offtrt,
postrandom=c("cd420","cd820","offtrt"), data=ACTG175, trt.id="treat",
endpoint="dichotomous")
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注：
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发表于 2012-9-30 14:56:55
