Description Usage Arguments Value Examples
Estimation of average treatment effect using the optimal linear combination method when misclassification probabilities are unknown but validation data are available
1 2 | EstValidation(maindata, validationdata, indA, indYerror, indX, indY,
confidence = 0.95)
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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 |
A list of the estimate of average treatment effect, sandwich-variance-based standard error, confidence interval, and the estimated sensitivity and specificity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #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)
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