#simulated data from a case-cohort design
data(CCHsimdata)
predict.time <- 0.75
## augmented ipw
AIPWmeasures( time = CCHsimdata$xi, event = CCHsimdata$di,
X = cbind(CCHsimdata$y1, CCHsimdata$y2),
subcohort = CCHsimdata$vi,
aug.weights.x = CCHsimdata$y1,
risk.threshold = c(.05, .3),
landmark.time = predict.time,
weight.method = 'Aug',
design = "CCH",
smoothing.par = 0.9,
calculate.sd = TRUE,
pnf.threshold = 0.85,
pcf.threshold = 0.2)
# true ipw
AIPWmeasures( time = CCHsimdata$xi, event = CCHsimdata$di,
X = cbind(CCHsimdata$y1, CCHsimdata$y2),
subcohort = CCHsimdata$vi,
#aug.weights.x = CCHsimdata$y1,
risk.threshold = c(.05, .3),
landmark.time = predict.time,
weight.method = 'True',
design = "CCH",
smoothing.par = 0.9,
calculate.sd = TRUE,
pnf.threshold = 0.85,
pcf.threshold = 0.8,
ncc.nmatch = 2)
#simulated data from a ncc design with nmatch = 2
data("NCCsimdata")
AIPWmeasures( time = NCCsimdata$xi, event = NCCsimdata$di,
X = cbind(NCCsimdata$y1, NCCsimdata$y2),
subcohort = NCCsimdata$vi,
aug.weights.x = NCCsimdata$y1,
risk.threshold = c(.01, .03),
landmark.time = predict.time,
weight.method = 'Aug',
design = "NCC",
smoothing.par = 0.9,
calculate.sd = TRUE,
pnf.threshold = 0.85,
pcf.threshold = 0.8,
ncc.nmatch = 2)
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