EstValidation {ipwErrorY}

Estimation of ATE with Validation Data

Description

Estimation of average treatment effect using the optimal linear combination method when misclassification probabilities are unknown but validation data are available

Usage

EstValidation(maindata, validationdata, indA, indYerror, indX, indY,
  confidence = 0.95)

Arguments

maindata

The non-validation main data in the form of R data frame without missing data

validationdata

The validation data in the form of R data frame without missing data

indA

A column name indicating the binary treatment variable

indYerror

A column name indicating the misclassified binary outcome variable

indX

A vector of column names indicating the covariates included in the treatment model

indY

A column name indicating the true binary outcome variable

confidence

The confidence level between 0 and 1; the default is 0.95 corresponding to a 95 per cent confidence interval

Value

A list of the estimate of average treatment effect, sandwich-variance-based standard error, confidence interval, and the estimated sensitivity and specificity

Examples

#create main data and validation data with sensitivity=0.95 and specificity=0.85
set.seed(100)
X1=rnorm(1200)   
A=rbinom(1200,1,1/(1+exp(-0.2-X1)))
Y=rbinom(1200,1,1/(1+exp(-0.2-A-X1)))
y1=which(Y==1)
y0=which(Y==0) 
Yast=Y
Yast[y1]=rbinom(length(y1),1,0.95)
Yast[y0]=rbinom(length(y0),1,0.15)
mainda=data.frame(A=A,X1=X1,Yast=Yast)
X1=rnorm(800)   
A=rbinom(800,1,1/(1+exp(-0.2-X1)))
Y=rbinom(800,1,1/(1+exp(-0.2-A-X1)))
y1=which(Y==1)
y0=which(Y==0) 
Yast=Y
Yast[y1]=rbinom(length(y1),1,0.95)
Yast[y0]=rbinom(length(y0),1,0.15)
validationda=data.frame(A=A,X1=X1,Y=Y,Yast=Yast)
head(mainda)
head(validationda)
#apply the optimal linear combination correction method
EstValidation(mainda,validationda,"A","Yast","X1","Y",0.95)