KnownError: Estimation of ATE with Known Error

In ipwErrorY: Inverse Probability Weighted Estimation of Average Treatment Effect with Misclassified Binary Outcome

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)

ipwErrorY documentation built on May 6, 2019, 1:04 a.m.