- Single time-point iterative TMLE (three causes)
- Single time-point one-step TMLE (three causes)
- Two time-points one-step TMLE (three causes)
Current implementation:
For simulation study:
All data contain three continuous covariates, L_1, L_2, L_3, a
binary treatment, A, a time-to-event variable, time, and an event
indicator, delta.
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(dt1 <- sim.data2(500, setting=1, m=101)) id time delta A L1 L2 L3 1: 1 2.0631444 1 0 0.49139281 -0.6688724 0.291922718 2: 2 2.1493063 1 0 -0.73285336 0.5850001 0.500198228 3: 3 1.4273563 1 0 -0.14371713 0.5780268 0.445078787 4: 4 0.4280762 1 1 -0.26399351 -0.3642358 0.450386852 5: 5 0.8844077 1 0 -0.38961309 0.3991256 0.867301825 --- 496: 496 0.2453018 1 1 0.22407892 0.9891128 0.980549826 497: 497 1.4340098 1 0 0.11135457 -0.7108160 0.006933557 498: 498 1.8405661 1 1 -0.91630937 -0.9992122 0.391603182 499: 499 0.7440901 0 1 -0.45349936 -0.6978073 0.435121550 500: 500 0.6051863 1 1 -0.01772394 0.5169929 0.821363185
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(dt2 <- sim.data2(500, setting=2, m=30033)) id time delta A L1 L2 L3 1: 1 0.9471819 1 0 0.756822031 0.77002296 0.7797066 2: 2 1.1323334 1 0 -0.065353703 0.35827118 0.7976352 3: 3 1.7053282 1 1 0.656070347 0.20709002 0.5780152 4: 4 0.8061263 1 1 -0.763604288 0.40173444 0.6358432 5: 5 0.8088335 1 1 0.875432958 0.01411658 0.9980470 --- 496: 496 0.7946299 1 1 -0.946018820 0.47053691 0.4194170 497: 497 0.8807391 1 0 -0.648760873 0.84350969 0.8831198 498: 498 1.2373449 1 1 -0.334965690 0.05032776 0.3214108 499: 499 0.7937388 1 1 -0.006926148 0.63071498 0.7458795 500: 500 0.1190475 1 0 0.820146986 0.84440805 0.5426060
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run0 <- contmle(dt1, #-- dataset
treat.effect="0", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("survival"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1)
)
)) $init
tau=0.5
init.est 0.08273712
init.se 0.01390123
$tmle
tau=0.5
tmle.est 0.04657883
tmle.se 0.01377620
$init
tau=0.5
init.est 0.08909946
init.se 0.01660139
$tmle
tau=0.5
tmle.est 0.06933829
tmle.se 0.01657499
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run1 <- contmle(dt1, #-- dataset
treat.effect="1", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("survival"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1)
)
)) $init
tau=0.5
init.est 0.08203692
init.se 0.02045089
$tmle
tau=0.5
tmle.est 0.11721628
tmle.se 0.02041782
$init
tau=0.5
init.est 0.07562680
init.se 0.01850529
$tmle
tau=0.5
tmle.est 0.09682654
tmle.se 0.01847456
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run2 <- contmle(dt1, #-- dataset
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("survival"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1)
)
)) $init
tau=0.5
init.est -0.0007001979
init.se 0.0246345062
$tmle
tau=0.5
tmle.est 0.07052852
tmle.se 0.02469477
$init
tau=0.5
init.est -0.01347265
init.se 0.02472451
$tmle
tau=0.5
tmle.est 0.02748492
tmle.se 0.02472948
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run3 <- contmle(dt1, #-- dataset
treat.effect="ate", #-- target the ate directly
tau=c(0.3, 0.5), #-- time-point of interest
estimation=list("survival"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1)
)
)) $init
tau=0.3 tau=0.5
init.est -0.000366087 -0.0007001979
init.se 0.017949756 0.0246345062
$tmle
tau=0.3 tau=0.5
tmle.est 0.04027219 0.07067547
tmle.se 0.01798117 0.02469414
$init
tau=0.3 tau=0.5
init.est -0.005064993 -0.01347265
init.se 0.015861703 0.02472451
$tmle
tau=0.3 tau=0.5
tmle.est 0.00265876 0.02741378
tmle.se 0.01586685 0.02472889
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run3 <- contmle(dt1, #-- dataset
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("survival"=list(fit="sl",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="sl",
model=Surv(time, delta==0)~L1+L2+L3+A*L1)
),
sl.models=list(
mod1=c(Surv(time, delta==1)~A+L1+L2+L3, changepoint=c(0.3, 0.7)),
mod2=c(Surv(time, delta==1)~A+L2.squared+L1*L2+L3, changepoint=NULL),
mod3=c(Surv(time, delta==1)~A+L1.squared+L1*L2+L3, changepoint=c(0.3, 0.7)),
mod4=c(Surv(time, delta==1)~A+L2.squared, changepoint=c(0.3, 0.7)),
mod5=c(Surv(time, delta==1)~A+L1.squared, changepoint=c(0.3, 0.7)),
mod6=c(Surv(time, delta==1)~A+L1.squared+L2+L3, changepoint=c(0.3, 0.7)),
mod7=c(Surv(time, delta==1)~A+L2.squared, changepoint=NULL),
mod8=c(Surv(time, delta==1)~A+L1.squared, changepoint=NULL),
mod9=c(Surv(time, delta==1)~A+L1+L2+L3, changepoint=NULL),
mod10=c(Surv(time, delta==1)~A*L1+L2+L3, changepoint=NULL),
mod11=c(Surv(time, delta==1)~A*L1.squared+L2+L3, changepoint=NULL)
),
verbose.sl=TRUE,
)) [1] "model picked for survival: A + L1.squared + L1 * L2 + L3"
[1] "changepoint picked: 0.7"
[1] "model picked for cens: A + L1.squared + L1 * L2 + L3"
[1] "changepoint picked: 0.3"
$init
tau=0.5
init.est 0.05088319
init.se 0.02514863
$tmle
tau=0.5
tmle.est 0.07081248
tmle.se 0.02516692
Warning message:
In fitter(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1 ; beta may be infinite.
In this small simulation study, we investigate one-step TMLE’s ability
to fit a monotone survival curve compared to the iterative TMLE. We
simple fit the treatment-specific survival curve at four time-points
where two of the time-points are very close to each other.
First, let’s look at the results from the one-step TMLE:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/make.table.fun.R")
make.table.fun(setting=1, tau=c(0.4, 0.5, 0.99, 1.00), target=1,
treat.effect="1", M=500, n=300, censoring.informative=TRUE)[1] "Estimate A=1 (n=300, M=500)"
$F
true.value bias sd cov rel.mse
tau=0.4 0.089757 0.0003486295 0.02342802 0.936 0.9886447
tau=0.5 0.128354 -0.0011479946 0.02740761 0.938 0.9931828
tau=0.99 0.284073 0.0006176577 0.03801468 0.946 0.9707157
tau=1 0.286628 0.0007756627 0.03814598 0.952 0.9733194
These look similar to what we achieve with the iterative TMLE:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/make.table.fun.R")
do.call("rbind", lapply(c(0.4, 0.5, 0.99, 1.00), function(tt)
make.table.fun(setting=1, tau=tt, target=1,
treat.effect="1", M=500, n=300, censoring.informative=TRUE)[[1]]
)) [1] "Estimate A=1 (n=300, M=500)"
[1] "Estimate A=1 (n=300, M=500)"
[1] "Estimate A=1 (n=300, M=500)"
[1] "Estimate A=1 (n=300, M=500)"
true.value bias sd cov rel.mse
tau=0.4 0.089899 0.00020970339 0.02342812 0.934 0.9912122
tau=0.5 0.128700 -0.00150778177 0.02740787 0.936 0.9937420
tau=0.99 0.284617 0.00004529501 0.03801478 0.946 0.9700442
tau=1 0.287066 0.00026965476 0.03814609 0.952 0.9744732
But let us also look at the monotonicity properties of the fitted probabilities. First we extract all the fitted values from the simulation study:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/make.table.fun.R")
run.one <- do.call("cbind", make.table.fun(setting=1, tau=c(0.4, 0.5, 0.99, 1.00), target=1,
output.est=TRUE,
treat.effect="1", M=500, n=300, censoring.informative=TRUE))
run.sep <- do.call("cbind", lapply(c(0.4, 0.5, 0.99, 1.00), function(tt)
make.table.fun(setting=1, tau=tt, target=1, output.est=TRUE,
treat.effect="1", M=500, n=300, censoring.informative=TRUE)[[1]]
))Then we look at monoticity:
mean(run.one.diff <- apply(run.one, 1, diff)<0)
mean(run.sep.diff <- apply(run.sep, 1, diff)<0) [1] 0 [1] 0.03266667
Thus, we see that the iterative TMLE does not always result in a
monotone curve. We do note, however, that in the present simulations
this mostly kicks in at the time-points very close to each other
(tau=0.99,1):
mean(run.sep.diff <- apply(run.sep[,3:4], 1, diff)<0)[1] 0.094
And almost never at the other time-points (tau=0.4,0.5):
mean(run.sep.diff <- apply(run.sep[,1:2], 1, diff)<0)[1] 0.004
In these cases, the fitted values look as follows (we only show the
worst examples for tau=0.99,1):
tau=0.99 tau=1 tmle.est 0.3442180 0.3440807 tmle.est 0.3073135 0.3070199 tmle.est 0.2965696 0.2962569 tmle.est 0.2816341 0.2815084 tmle.est 0.2554443 0.2552757 tmle.est 0.2377091 0.2375831 tmle.est 0.2749989 0.2748831 tmle.est 0.2861972 0.2859966 tmle.est 0.2682481 0.2680999 tmle.est 0.3029685 0.3028329 tmle.est 0.2756541 0.2754998 tmle.est 0.2520386 0.2518931
With corresponding one-step fitted values:
tau=0.99 tau=1 tmle.est 0.3439312 0.3446871 tmle.est 0.3070050 0.3077063 tmle.est 0.2961468 0.2969222 tmle.est 0.2810581 0.2818064 tmle.est 0.2550053 0.2556832 tmle.est 0.2373026 0.2379165 tmle.est 0.2747290 0.2753992 tmle.est 0.2855378 0.2863071 tmle.est 0.2679179 0.2685774 tmle.est 0.3026780 0.3033653 tmle.est 0.2752695 0.2760548 tmle.est 0.2519170 0.2526037
And:
tau=0.4 tau=0.5 tmle.est 0.07731765 0.07718782 tmle.est 0.13481901 0.13480390
With corresponding one-step fitted values:
tau=0.4 tau=0.5 tmle.est 0.0769099 0.07770785 tmle.est 0.1343741 0.13526776
All data contain three continuous covariates, L_1, L_2, L_3, a
binary treatment, A, a time-to-event variable, time, and an event
indicator, delta.
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(dt1 <- sim.data2(500, setting=1, competing.risk=TRUE)) id time delta A L1 L2 L3 1: 1 0.2874422 1 0 0.30975138 -0.7685858 0.3152207 2: 2 0.3277389 1 0 0.02027468 0.5996824 0.7876355 3: 3 0.5929084 1 0 -0.56207753 -0.8157896 0.4990356 4: 4 0.3566947 2 0 -0.23240904 0.6263003 0.9797428 5: 5 0.4022813 1 1 0.42003211 -0.5027374 0.8166309 --- 496: 496 0.8319554 2 1 0.70791783 -0.7384621 0.1793389 497: 497 0.5464264 2 0 0.33327201 0.5171843 0.1297940 498: 498 0.7545253 2 0 -0.18518105 0.4249485 0.4925814 499: 499 0.5686617 1 1 -0.38356117 0.9970010 0.1349552 500: 500 0.5134096 1 1 -0.11580208 0.3356330 0.3555878
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(dt2 <- sim.data2(500, setting=2, competing.risk=TRUE)) id time delta A L1 L2 L3 1: 1 0.5799401 2 1 0.61881053 0.4555461 0.9244269 2: 2 0.6195841 0 1 -0.05301504 0.9538462 0.5191956 3: 3 0.3976385 2 0 0.81225760 0.8830862 0.2465510 4: 4 0.1252781 1 1 0.80605090 0.1536068 0.6741928 5: 5 0.1745883 1 1 0.95105817 0.6554411 0.9900094 --- 496: 496 0.4873752 2 0 -0.01904145 0.8212517 0.8391338 497: 497 0.1826586 0 0 -0.66730849 0.9426368 0.8602731 498: 498 0.6606991 2 1 -0.36369797 0.9727633 0.8323750 499: 499 0.4425950 1 0 0.23650685 0.9604297 0.3021334 500: 500 0.7447092 1 1 -0.66147132 0.2512112 0.5502155
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(dt3 <- sim.data2(500, setting=2, no.cr=3, competing.risk=TRUE)) id time delta A L1 L2 L3 1: 1 0.1450495 2 0 0.18646167 0.53803176 0.9218310 2: 2 0.5472824 3 1 -0.91214117 0.18023776 0.2624584 3: 3 0.4206132 3 1 0.26982345 0.63716911 0.1119786 4: 4 0.6061732 1 1 -0.52404887 0.68882887 0.4691299 5: 5 0.5278644 2 1 0.08703335 0.61165321 0.9285522 --- 496: 496 0.2583767 0 1 0.32032951 0.99817887 0.8142770 497: 497 0.6388712 3 1 0.97519593 0.02428083 0.1036670 498: 498 0.3337664 1 1 0.81967845 0.10287961 0.7454781 499: 499 0.1383786 2 0 0.23112059 0.68527974 0.5222549 500: 500 0.1981358 0 0 -0.14390677 0.85631816 0.5716522
id time delta A L1 L2 L3 1: 1 0.2104011 2 1 0.47499590 0.507529442 0.5081989 2: 2 0.9950924 2 0 -0.18157279 0.465752728 0.6365665 3: 3 0.0962248 1 0 -0.72525429 0.303056284 0.9455085 4: 4 0.6079188 1 1 -0.99947581 0.691318560 0.3090190 5: 5 0.6786964 0 0 -0.57499470 0.990865805 0.7190923 --- 496: 496 0.9254278 2 1 -0.32272306 0.754394145 0.2812119 497: 497 1.3892085 1 1 -0.49018167 0.724425462 0.4775683 498: 498 0.7920518 0 0 -0.09834086 0.534232647 0.8407960 499: 499 0.5770960 2 0 0.12161625 0.123686559 0.2523931 500: 500 0.4539557 0 1 0.51500226 0.006624758 0.3918125
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run1 <- contmle(dt2, #-- dataset
target=1, #-- go after cause 1 specific risk
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.5
init.est 0.007793466
init.se 0.040004181
$tmle
$tmle$F1
tau=0.5
tmle.est 0.003432847
tmle.se 0.040002785
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run2 <- contmle(dt2, #-- dataset
target=1:2, #-- go after cause 1 and cause 2 specific risks
iterative=TRUE, #-- use iterative tmle to target F1 and F2 separately
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.5
init.est 0.007793466
init.se 0.040004181
$init$F2
tau=0.5
init.est -0.09378281
init.se 0.03692451
$tmle
$tmle$F1
tau=0.5
tmle.est 0.003432847
tmle.se 0.040002785
$tmle$F2
tau=0.5
tmle.est -0.08613062
tmle.se 0.03692450
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run3 <- contmle(dt2, #-- dataset
target=1:2, #-- go after cause 1 and cause 2 specific risks
iterative=FALSE, #-- use one-step tmle to target F1 and F2 simultaneously
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.5
init.est 0.007793466
init.se 0.040004181
$init$F2
tau=0.5
init.est -0.09378281
init.se 0.03692451
$tmle
$tmle$F1
tau=0.5
tmle.est 0.003542553
tmle.se 0.040002546
$tmle$F2
tau=0.5
tmle.est -0.08624101
tmle.se 0.03692414
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run4 <- contmle(dt2, #-- dataset
target=1:2, #-- go after cause 1 and cause 2 specific risks
iterative=FALSE, #-- use one-step tmle to target F1 and F2 simultaneously
treat.effect="ate", #-- target the ate directly
tau=c(0.3, 0.5), #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.3 tau=0.5
init.est -0.001742215 0.007793466
init.se 0.028741879 0.040004181
$init$F2
tau=0.3 tau=0.5
init.est -0.05149133 -0.09378281
init.se 0.02771378 0.03692451
$tmle
$tmle$F1
tau=0.3 tau=0.5
tmle.est 0.01948492 0.003680884
tmle.se 0.02874492 0.040002053
$tmle$F2
tau=0.3 tau=0.5
tmle.est -0.04689139 -0.08623811
tmle.se 0.02771312 0.03692375
Here we have a dataset with three causes, but we only target the cause-1 and cause-2 risks:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run5 <- contmle(dt3, #-- dataset (with three competing risks)
target=1:2, #-- go after cause 1 and cause 2 specific risks
iterative=FALSE, #-- use one-step tmle to target F1 and F2 simultaneously
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3),
"cause3"=list(fit="cox",
model=Surv(time, delta==3)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.5
init.est -0.007292943
init.se 0.036790910
$init$F2
tau=0.5
init.est 0.001042136
init.se 0.035002328
$tmle
$tmle$F1
tau=0.5
tmle.est 0.01112978
tmle.se 0.03679016
$tmle$F2
tau=0.5
tmle.est 0.009518483
tmle.se 0.035000835
Similarly, we can target all three cause-specific risks simultaneously:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
(run6 <- contmle(dt3, #-- dataset (with three competing risks)
target=1:3, #-- go after all three cause-specific risks
iterative=FALSE, #-- use one-step tmle to target F1, F2 and F3 simultaneously
treat.effect="ate", #-- target the ate directly
tau=0.5, #-- time-point of interest
estimation=list("cause1"=list(fit="cox",
model=Surv(time, delta==1)~A+L1.squared),
"cens"=list(fit="cox",
model=Surv(time, delta==0)~L1+L2+L3+A*L1),
"cause2"=list(fit="cox",
model=Surv(time, delta==2)~A+L1+L2+L3),
"cause3"=list(fit="cox",
model=Surv(time, delta==3)~A+L1+L2+L3)
)
)) $init
$init$F1
tau=0.5
init.est -0.007292943
init.se 0.036790910
$init$F2
tau=0.5
init.est 0.001042136
init.se 0.035002328
$init$F3
tau=0.5
init.est -0.04157675
init.se 0.03528795
$tmle
$tmle$F1
tau=0.5
tmle.est 0.01130622
tmle.se 0.03679005
$tmle$F2
tau=0.5
tmle.est 0.009468111
tmle.se 0.035000793
$tmle$F3
tau=0.5
tmle.est -0.04573218
tmle.se 0.03528728
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
test1 <- run.fun(M=1, n=1000, competing.risk=TRUE,
target=1, tau=0.5,
setting=2,
censoring.informative=TRUE,
iterative=TRUE,
no_cores=1) [1] "m=1"
$`m=1`
$`m=1`$init
$`m=1`$init$F1
tau=0.5
init.est -0.06945745
init.se 0.02777824
$`m=1`$km
$`m=1`$km$F1
tau=0.5
km.est -0.03353986
km.se 0.02819749
$`m=1`$tmle
$`m=1`$tmle$F1
tau=0.5
tmle.est -0.04757723
tmle.se 0.02778435
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/make.table.fun.R")
make.table.fun(setting=2, tau=0.5, target=1:3, cr3=TRUE,
fit.outcome="cox", fit.cr="cox", fit.cens="cox", M=500, n=300,
iterative=TRUE, competing.risk=TRUE, misspecify.outcome=FALSE,
censoring.informative=TRUE) [1] "Estimate ATE (n=300, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.5 -0.007538 -0.005335258 0.04930792 0.956 1.018455
$F2
true.value bias sd cov rel.mse
tau=0.5 -0.050232 -0.0003879127 0.04730437 0.95 1.038196
$F3
true.value bias sd cov rel.mse
tau=0.5 -0.050664 0.001266594 0.04766 0.944 1.04061
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/make.table.fun.R")
make.table.fun(setting=2, tau=0.5, target=1:3, cr3=TRUE,
fit.outcome="sl", fit.cr="sl", fit.cens="sl", M=500, n=300,
iterative=TRUE, competing.risk=TRUE,
censoring.informative=TRUE) [1] "Estimate ATE (n=300, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.5 -0.007538 -0.005419215 0.04986311 0.96 1.028335
$F2
true.value bias sd cov rel.mse
tau=0.5 -0.050232 -0.001210021 0.04834965 0.942 1.085933
$F3
true.value bias sd cov rel.mse
tau=0.5 -0.050664 0.0008989421 0.04883367 0.952 1.092358
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/summary.fun.R")
summary.fun(setting=2, tau=0.5, target=1:3, cr3=TRUE,
fit.outcome="sl", fit.cr="sl", fit.cens="sl", M=500, n=1000,
iterative=FALSE, competing.risk=TRUE,
censoring.informative=TRUE) [1] "Estimate ATE (n=1000, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.5 -0.007538 0.0001793097 0.0269632 0.94 0.970743
$F2
true.value bias sd cov rel.mse
tau=0.5 -0.050232 -0.0003145141 0.02601505 0.958 1.033968
$F3
true.value bias sd cov rel.mse
tau=0.5 -0.050664 0.0004003152 0.02617214 0.944 0.9890767
Unweighted inner product:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/summary.fun.R")
summary.fun(setting=2, tau=c(0.35, 0.5), target=1:3, cr3=TRUE,
fit.outcome="cox", fit.cr="cox", fit.cens="cox", M=500, n=300,
iterative=FALSE, competing.risk=TRUE,
censoring.informative=TRUE) [1] "Estimate ATE (n=300, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.35 -0.010336 -0.004990964 0.04087722 0.954 0.8617386
tau=0.5 -0.006968 -0.006094558 0.04930366 0.954 1.0094925
$F2
true.value bias sd cov rel.mse
tau=0.35 -0.03870 -0.0002046165 0.03882936 0.956 0.8947348
tau=0.5 -0.05009 -0.0003326479 0.04728705 0.950 1.0062482
$F3
true.value bias sd cov rel.mse
tau=0.35 -0.039266 0.001567532 0.03911725 0.968 0.9006001
tau=0.5 -0.050453 0.001381332 0.04766251 0.946 1.0056505
Here we have used the inner product weighted by the standard error of the efficient influence function:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/summary.fun.R")
summary.fun(setting=2, tau=c(0.35, 0.5), target=1:3, cr3=TRUE,
fit.outcome="cox", fit.cr="cox", fit.cens="cox", M=500, n=300,
iterative=FALSE, competing.risk=TRUE, weighted.norm="sigma",
censoring.informative=TRUE) [1] "Estimate ATE (n=300, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.35 -0.010336 -0.004747987 0.04087767 0.952 0.8900914
tau=0.5 -0.006968 -0.005926739 0.04930502 0.964 1.0011447
$F2
true.value bias sd cov rel.mse
tau=0.35 -0.03870 -0.0003789272 0.03882859 0.954 0.9263194
tau=0.5 -0.05009 -0.0004961173 0.04728644 0.948 1.0014458
$F3
true.value bias sd cov rel.mse
tau=0.35 -0.039266 0.001551587 0.03911732 0.964 0.9292563
tau=0.5 -0.050453 0.001387621 0.04766632 0.946 0.9986212
Inner product weighted by covariance matrix:
# NOTE: you need to set the working directory
source("./simulation-contmle/load.R")
source("./simulation-contmle/summary.fun.R")
summary.fun(setting=2, tau=c(0.35, 0.5), target=1:3, cr3=TRUE,
fit.outcome="cox", fit.cr="cox", fit.cens="cox", M=500, n=300,
iterative=FALSE, competing.risk=TRUE, weighted.norm="Sigma",
censoring.informative=TRUE) [1] "Estimate ATE (n=300, M=500)"
$F1
true.value bias sd cov rel.mse
tau=0.35 -0.010336 -0.004954027 0.04088965 0.9519038 0.9733054
tau=0.5 -0.006968 -0.006118715 0.04930854 0.9599198 0.9947518
$F2
true.value bias sd cov rel.mse
tau=0.35 -0.03870 0.00007250139 0.03883329 0.9458918 0.9534236
tau=0.5 -0.05009 -0.00042490968 0.04729003 0.9539078 0.9871664
$F3
true.value bias sd cov rel.mse
tau=0.35 -0.039266 0.001809895 0.03912174 0.9519038 0.9826952
tau=0.5 -0.050453 0.001003023 0.04765887 0.9539078 0.9824652
The code has been tested with the following R version
_ platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status major 4 minor 0.2 year 2020 month 06 day 22 svn rev 78730 language R version.string R version 4.0.2 (2020-06-22) nickname Taking Off Again
and the following package versions:
| Package | Version |
|---|---|
| data.table | 1.13.0 |
| zoo | 1.8.8 |
| stringr | 1.4.0 |
| ltmle | 1.2.0 |
| parallel | 4.0.2 |
| foreach | 1.5.0 |
| doParallel | 1.0.15 |