KnownError {ipwErrorY}

Estimation of ATE with Known Error

Description

Estimation of average treatment effect with known outcome misclassification probabilities, i.e., known sensitivity and specificity

Usage

KnownError(data, indA, indYerror, indX, sensitivity, specificity,
  confidence = 0.95)

Arguments

data

The dataset to be analyzed 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

sensitivity

The specified sensitivity between 0 and 1

specificity

The specified specificity between 0 and 1

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 and confidence interval

Examples

#create a dataset with sensitivity=0.95 and specificity=0.85
set.seed(100)
X1=rnorm(2000) 
A=rbinom(2000,1,1/(1+exp(-0.2-X1)))
Y=rbinom(2000,1,1/(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)
da=data.frame(X1=X1,A=A,Yast=Yast)
head(da)
#apply the correction method with sensitivity=0.95 and specificity=0.85
KnownError(da,"A","Yast","X1",0.95,0.85,0.95)