eval_squant {squant}

SQUANT performance evaluation

Description

eval_squant evaluates the subgroup identification performance.

Usage

eval_squant(
  yvar,
  censorvar,
  trtvar,
  trtcd = 1,
  dir,
  type,
  data,
  squant.out,
  brief = FALSE
)

Arguments

yvar

A character. The response variable name in the data. The corresponding column in the data should be numeric.

censorvar

A character or NULL. The event indicator variable name in the data. The corresponding column in the data should be 0(censor) or 1(event). Use NULL when it is not a time-to-event case.

trtvar

A character or NULL. The trt variable name in the data for the predictive case. The corresponding column in the data should contain the treatment assignments, and can be either numeric or character. Use NULL for the prognostics case.

trtcd

The code for the treatment arm for the predictive case, e.g. trtcd="treatment" or trtcd=1, etc.

dir

A character, "larger" or "smaller". When dir == "larger", larger response is preferred for the target subgroup. In the predictive case, it means the derived signature from squant selects patients satisfying E(Y|X,TRT)-E(Y|X,CTRL)>=quant. In the prognostic case, it means the derived signature from squant selects patients satisfying E(Y|X)>=quant. When dir == "smaller", smaller response is preferred for the target subgroup. In the predictive case, it means the derived signature from squant selects patients satisfying E(Y|X,CTRL)-E(Y|X,TRT)>=quant. In the prognostic case, it means the derived signature from squant selects patients satisfying E(Y|X)<=quant.

type

The response type. Use "s" for survival, "b" for binary, and "c" for continuous.

data

The data frame for performance evaluation of the derived signature.

squant.out

The squant object, the signature of which will be applied to the specified data. The output of squant function.

brief

A logical value, TRUE or FALSE. When TRUE, only the most important p value will be reported.

Details

This function evaluates the subgroup identification performance through applying the derived signature (the squant object) to a specified dataset. Note that when the specified dataset is the same as the training set, the performance is usually over-optimistic and is subject to over-fitting. Ideally, use an independent testing set to have an honest evaluation of the performance.

Value

An object of "eval_squant". A list containing the following elements.

inter.pval

Treatment*subgroup Interaction p value (predictive case only, adjusted for prognostic markers if any).

pos.group.pval

The p value of the trt difference in the selected positive group (predictive case only, adjusted for prognostic markers if any).

neg.group.pval

The p value of the trt difference in the negative group (predictive case only, adjusted for prognostic markers if any).

pval

The p value of group comparison (prognostic case only).

group.stats

The performance of each arm by group (predictive case) or the performance of each group (prognostic case).

data.pred

The data with the predicted subgroup in the last column.

Examples

#toy example#
set.seed(888)
x=as.data.frame(matrix(rnorm(200),100,2))
names(x) = c("x1", "x2")
trt = sample(0:1, size=100, replace=TRUE)
y= 2*x[,2]*trt+rnorm(100)
data = cbind(y=y, trt=trt, x)
res = squant(yvar="y", censorvar=NULL, xvars=c("x1", "x2"),
             trtvar="trt", trtcd=1, data=data, type="c", weight=NULL,
             dir="larger", quant=NULL, xvars.keep=NULL, alpha=1,
             fold=2, n.cv = 10, FDR = 0.1, progress=FALSE)



#predictive case with continuous response#
set.seed(888)
x=as.data.frame(matrix(rnorm(20000),200,100))
names(x) = paste("x", 1:100,sep="")
trt = sample(0:1, size=200, replace=TRUE)
y=x[,1]+x[,2]*trt+rnorm(200)
data = cbind(y=y, trt=trt, x)
res = squant(yvar="y", censorvar=NULL, xvars=paste("x", 1:100,sep=""),
             trtvar="trt", trtcd=1, data=data, type="c", weight=NULL,
             dir="larger", quant=NULL, xvars.keep=NULL, alpha=1,
             fold=5, n.cv = 50, FDR = 0.1)
res
#fitted signature#
res$squant.fit
#performance of the identified subgroup#
#including:
#  interaction p value,
#  p valve of trt difference in positive group,
#  p value of trt difference in negative group,
#  and stats for each arm in each group.
res$performance
#interpretation#
res$interpretation1
res$interpretation2

#evaluation of prediction performance#
eval.res = eval_squant(yvar="y", censorvar=NULL, trtvar="trt", trtcd=1, dir="larger",
                       type="c", data=data, squant.out=res, brief=FALSE)
#plot the subgroups#
plot(res, trt.name="Trt", ctrl.name="Ctrl")
plot(eval.res, trt.name="Trt", ctrl.name="Ctrl")



#prognostic case with survival response#
set.seed(888)
x=as.data.frame(matrix(rnorm(20000),200,100))
names(x) = paste("x", 1:100,sep="")
y=10*(10+x[,1]+rnorm(200))
data = cbind(y=y, x)
data$event = sample(c(rep(1,150),rep(0,50)))
res = squant(yvar="y", censorvar="event", xvars=paste("x", 1:100,sep=""),
             trtvar=NULL, trtcd=NULL, data=data, type="s", weight=NULL,
             dir="larger", quant=NULL, xvars.keep=NULL, alpha=1,
             fold=5, n.cv = 50, FDR = 0.1)
res

#fitted signature#
res$squant.fit
#performance of the identified subgroup#
res$performance
#evaluation of prediction performance#
eval.res = eval_squant(yvar="y", censorvar="event", trtvar=NULL, trtcd=NULL, dir="larger",
                       type="s", data=data, squant.out=res, brief=FALSE)

#plot the subgroups#
plot(res, trt.name=NULL, ctrl.name=NULL)
plot(eval.res, trt.name=NULL, ctrl.name=NULL)