Description Usage Arguments Details Value References See Also Examples
speffSurv
conducts estimation and testing of the treatment effect in a twogroup randomized
clinical trial with a rightcensored timetoevent endpoint. It improves efficiency by leveraging baseline predictors
of the endpoint.
1 2 3 4 
formula 
a formula object with the response variable on the left of the 
data 
a data frame in which to interpret the variables named in the 
force.in 
a vector of indices to columns of the design matrix that should be included in each regression model. 
nvmax 
the maximum number of covariates considered for inclusion in a model. The default is 9. 
method 
specifies the type of search technique used in the model selection procedure carried out by the

optimal 
specifies the optimization criterion for model selection. The default is " 
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. 
conf.level 
the confidence level to be used for confidence intervals reported by 
fixed 
logical value; if 
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.
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.
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
.
speffSurv
does not allow missing values in the data.
speffSurv
returns an object of class "speffSurv
" which can be processed by
summary.speffSurv
to obtain or print a summary of the results. An object of class "speffSurv
"
is a list containing the following components:
beta 
a numeric vector with estimates of the treatment effect from the unadjusted proportional hazards model and the semiparametric efficient model using baseline covariates, respectively. 
varbeta 
a numeric vector of variance estimates for the treatment effect estimates in 
formula 
a list with components 
fixed 
a logical value; if 
conf.level 
confidence level of the confidence intervals reported by 
method 
search technique employed in the model selection procedure. 
n 
number of subjects in each treatment group. 
Lu X, Tsiatis AA. (2008), "Improving the efficiency of the logrank test using auxiliary covariates.", Biometrika, 95:679–694.
Tsiatis AA. (2006), Semiparametric Theory and Missing Data., New York: Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13  str(ACTG175)
data < na.omit(ACTG175[ACTG175$arms<=1 & ACTG175$gender==0,])
### efficiencyimproved estimation of log hazard ratio using
### baseline covariates
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;
### automated selection procedure is skipped
fit2 < speffSurv(Surv(days,cens) ~ cd40+cd80+age+wtkg+drugs+karnof+z30+
preanti+symptom, data=data, trt.id="arms", fixed=TRUE)

Loading required package: leaps
Loading required package: survival
'data.frame': 2139 obs. of 27 variables:
$ pidnum : int 10056 10059 10089 10093 10124 10140 10165 10190 10198 10229 ...
$ age : int 48 61 45 47 43 46 31 41 40 35 ...
$ wtkg : num 89.8 49.4 88.5 85.3 66.7 ...
$ hemo : int 0 0 0 0 0 0 0 0 0 0 ...
$ homo : int 0 0 1 1 1 1 1 1 1 1 ...
$ drugs : int 0 0 1 0 0 1 0 1 0 0 ...
$ karnof : int 100 90 90 100 100 100 100 100 90 100 ...
$ oprior : int 0 0 0 0 0 0 0 0 0 0 ...
$ z30 : int 0 1 1 1 1 1 1 1 1 1 ...
$ zprior : int 1 1 1 1 1 1 1 1 1 1 ...
$ preanti: int 0 895 707 1399 1352 1181 930 1329 1074 964 ...
$ race : int 0 0 0 0 0 0 0 0 0 0 ...
$ gender : int 0 0 1 1 1 1 1 1 1 1 ...
$ str2 : int 0 1 1 1 1 1 1 1 1 1 ...
$ strat : int 1 3 3 3 3 3 3 3 3 3 ...
$ symptom: int 0 0 0 0 0 0 0 0 1 0 ...
$ treat : int 1 1 1 1 0 1 0 0 1 0 ...
$ offtrt : int 0 0 1 0 0 0 0 0 1 1 ...
$ cd40 : int 422 162 326 287 504 235 244 401 214 221 ...
$ cd420 : int 477 218 274 394 353 339 225 366 107 132 ...
$ cd496 : int 660 NA 122 NA 660 264 106 453 8 NA ...
$ r : int 1 0 1 0 1 1 1 1 1 0 ...
$ cd80 : int 566 392 2063 1590 870 860 708 889 652 221 ...
$ cd820 : int 324 564 1893 966 782 1060 699 720 131 759 ...
$ cens : int 0 1 0 0 0 0 1 0 1 1 ...
$ days : int 948 1002 961 1166 1090 1181 794 957 198 188 ...
$ arms : int 2 3 3 3 0 1 0 0 3 0 ...
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