Nothing
require(LocalControlStrategy)
# Input plasmode data on Blood Thinner use in Percutaneous Coronary Interventions (PCIs).
# This simulation is based upon data collected at the Lindner Center: Kereiakes et al. (2000).
data(pci15k)
# outcomes: surv6mo (binary) or cardcost (continuous)
# treatment: thin ...is binary
# Define Cluster Hierarchy for UNSUPERVISED, nonparametric analyses...
xvars <- c("stent","height","female","diabetic","acutemi","ejfract","ves1proc")
system.time( hclobj <- LCcluster(pci15k, xvars) ) # Calculations take ~8 seconds.
hclobj # defalut clustering method = "ward.D"
plot(hclobj)
# Save Local Control basic parameter settings to an environment that will be updated...
LCe <- LCsetup(hclobj, pci15k, thin, surv6mo)
ls.str(LCe)
# Compute and Save LTD distributions for several values of K = number of Clusters...
surv0050 <- ltdagg(50, LCe)
surv0100 <- ltdagg(100, LCe)
surv0200 <- ltdagg(200, LCe)
surv0500 <- ltdagg(500, LCe) # average cluster size ~31 patients
plot(surv0500, show="ecdf", LCe)
surv0750 <- ltdagg(750, LCe)
surv1000 <- ltdagg(1000, LCe) # average cluster size ~16 patients
plot(surv1000, show="ecdf", LCe)
# "Sensitivity Analysis" Summary...
LCcompare(LCe)
# LTD Distribution for 500 Clusters appears to Optimize Variance-Bias Trade-Offs...
# Save and plot Instrumental Variable LAOs for 2 large values of K = number of Clusters...
iv0500 <- ivadj(surv0500)
plot(iv0500)
iv1K <- ivadj(surv1000)
plot(iv1K)
# Confirm: Does the Observed LRC distribution for 500 clusters truly differ from
# the Random Permutation NULL distribution assuming x_Covariates are Ignorable?
system.time( conf5H <- confirm(surv0500) ) # Simulation takes ~36 seconds.
conf5H
plot(conf5H)
# Simulate crude pmax.value for Kolmogorov-Smirnov D-statistic...
system.time( ksd5H <- KSperm(conf5H) ) # Simulation takes ~1.3 minutes.
ksd5H
plot(ksd5H)
# Example: "Most-Like-Me" Visualizations for pci15k Patient No. 11870...
# xvars: "stent","height","female","diabetic","acutemi","ejfract","ves1proc"
xvec11870 <- c( 0, 162, 1, 1, 0, 57, 1)
mlme11870 <- mlme(LCe, hclobj, surv0500, xvec11870 )
plot(mlme11870, NN = 250)
# Summary Statistics for 3 values of NN...
mlme.stats(mlme11870, NN = c( 250, 500, 1000 ))
### End of demo(pci15k) ##############################################
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.