KnownErrorDR {ipwErrorY}

Doubly Robust Estimation of ATE with Known Error

Description

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

Usage

KnownErrorDR(data, indA, indYerror, indXtrt, indXout, sensitivity, specificity,
  sharePara = FALSE, 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

indXtrt

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

indXout

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

sensitivity

The specified sensitivity between 0 and 1

specificity

The specified specificity between 0 and 1

sharePara

if the treated and untreated groups share parameters for covariates in the logistic outcome model (i.e., assuming Y~ T+X), then set sharePara=TRUE; if not (i.e., modeling Y~ X for the treated and untreated groups separately), then set sharePara=FALSE. By default, sharePara=FALSE

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)
X=rnorm(2000)   
xx=X^2
A=rbinom(2000,1,1/(1+exp(-0.1-X-0.2*xx)))
Y=rbinom(2000,1,1/(1+exp(1-A-0.5*X-xx)))
y1=which(Y==1)
y0=which(Y==0) 
Y[y1]=rbinom(length(y1),1,0.95)
Y[y0]=rbinom(length(y0),1,0.15)
Yast=Y
da=data.frame(A=A,X=X,xx=xx,Yast=Yast)
head(da)
#apply the doubly robust correction method with sensitivity=0.95 and specificity=0.85
KnownErrorDR(da,"A","Yast",c("X","xx"),c("X","xx"),0.95,0.85,FALSE,0.95)