Two-Stage Randomization for for Competing risks and Survival outcomes

Under two-stage randomization we can estimate the average treatment effect \(E(Y(i,\bar k))\) of treatment regime \((i,\bar k)\).

treatment A0=i and
- for all responses, randomization A1 = (k_1), so treatment k_1
- response*A1 = (k_1, k_2), so treatment k_1 if response 1, and treatment k_2 if response 2.

The estimator can be agumented in different ways: using the two randomizations and the dynamic censoring augmentatation.

Estimating \(\mu_{i,\bar k} = P(Y(i,\bar k,\epsilon=v) <= t)\), restricted mean \(E( \min(Y(i,\bar k),\tau))\) or years lost \(E( I(\epsilon=v) \cdot (\tau - \min(Y(i,\bar k),\tau)))\) using IPCW weighted estimating equations : \

The solved estimating eqution is \[\begin{align*} \sum_i \frac{I(min(T_i,t) < G_i)}{G_c(min(T_i ,t))} I(T \leq t, \epsilon=1 ) - AUG_0 - AUG_1 + AUG_C - p(i,j)) = 0 \end{align*}\] using the covariates from augmentR0 to augment with \[\begin{align*} AUG_0 = \frac{A_0(i) - \pi_0(i)}{ \pi_0(i)} X_0 \gamma_0 \end{align*}\] and using the covariates from augmentR1 to augment with \[\begin{align*} AUG_1 = \frac{A_0(i)}{\pi_0(i)} \frac{A_1(j) - \pi_1(j)}{ \pi_1(j)} X_1 \gamma_1 \end{align*}\] and censoring augmenting with \[\begin{align*} AUG_C = \int_0^t \gamma_c(s)^T (e(s) - \bar e(s)) \frac{1}{G_c(s) } dM_c(s) \end{align*}\] where \(\gamma_c(s)\) is chosen to minimize the variance given the dynamic covariates specified by augmentC.

The treatment’s must be given as factors.
Treatment for 2nd randomization may depend on response.
- Treatment probabilities are estimated by default and uncertainty from this adjusted for.
Randomization augmentation for 1’st and 2’nd randomization possible.
Censoring model possibly stratified on observed covariates (at time 0).
Censoring augmentation done dynamically over time with time-dependent covariates.

Standard errors are estimated using the influence function of all estimators and tests of differences can therefore be computed subsequently.

Data must be given on start,stop,status survival format with

one code of status indicating response, that is 2nd randomization
other codes defines the outcome of interest

library(mets) 
set.seed(100)

n <- 200
ddf <- mets:::gsim(n,covs=1,null=0,cens=1,ce=1,betac=c(0.3,1))
true <- apply(ddf$TTt<2,2,mean)
true
#> [1] 0.740 0.715 0.340 0.350
datat <- ddf$datat
## set-random response on data, only relevant after status==2 
response <- rbinom(n,1,0.5)
datat$response <- as.factor(response[datat$id]*datat$Count2)
datat$A000 <- as.factor(1)
datat$A111 <- as.factor(1)

bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.f~A0.f,
        augmentR1=~X11+X12+TR, augmentR0=~X01+X02,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f))
bb
#> Simple estimator :
#>                              coef           
#> A0.f=1, response*A1.f=1 0.5723748 0.19721139
#> A0.f=1, response*A1.f=2 0.7354573 0.08755875
#> A0.f=2, response*A1.f=1 0.2834829 0.10028201
#> A0.f=2, response*A1.f=2 0.4790373 0.10007200
#> 
#> First Randomization Augmentation :
#>                              coef           
#> A0.f=1, response*A1.f=1 0.5900338 0.21419118
#> A0.f=1, response*A1.f=2 0.7428143 0.08889887
#> A0.f=2, response*A1.f=1 0.2721992 0.10288280
#> A0.f=2, response*A1.f=2 0.4671886 0.10273188
#> 
#> Second Randomization Augmentation :
#>                              coef           
#> A0.f=1, response*A1.f=1 0.5564110 0.24533756
#> A0.f=1, response*A1.f=2 0.7185240 0.09841671
#> A0.f=2, response*A1.f=1 0.2778309 0.09021056
#> A0.f=2, response*A1.f=2 0.4685339 0.09839184
#> 
#> 1st and 2nd Randomization Augmentation :
#>                              coef           
#> A0.f=1, response*A1.f=1 0.5981472 0.26485855
#> A0.f=1, response*A1.f=2 0.7302633 0.10170090
#> A0.f=2, response*A1.f=1 0.2699247 0.09093482
#> A0.f=2, response*A1.f=2 0.4620365 0.09859532

estimate(coef=bb$riskG$riskG01[,1],vcov=crossprod(bb$riskG.iid$riskG01))
#>                         Estimate Std.Err    2.5%  97.5%   P-value
#> A0.f=1, response*A1.f=1   0.5981 0.26486 0.07903 1.1173 2.392e-02
#> A0.f=1, response*A1.f=2   0.7303 0.10170 0.53093 0.9296 6.946e-13
#> A0.f=2, response*A1.f=1   0.2699 0.09093 0.09170 0.4482 2.994e-03
#> A0.f=2, response*A1.f=2   0.4620 0.09860 0.26879 0.6553 2.783e-06
estimate(coef=bb$riskG$riskG01[,1],vcov=crossprod(bb$riskG.iid$riskG01),f=function(p) c(p[1]/p[2],p[3]/p[4]))
#>                         Estimate Std.Err   2.5% 97.5%  P-value
#> A0.f=1, response*A1.f=1   0.8191  0.3600 0.1135 1.525 0.022884
#> A0.f=2, response*A1.f=1   0.5842  0.2249 0.1433 1.025 0.009399
estimate(coef=bb$riskG$riskG01[,1],vcov=crossprod(bb$riskG.iid$riskG01),f=function(p) c(p[1]-p[2],p[3]-p[4]))
#>                         Estimate Std.Err    2.5%   97.5% P-value
#> A0.f=1, response*A1.f=1  -0.1321   0.266 -0.6534 0.38920  0.6194
#> A0.f=2, response*A1.f=1  -0.1921   0.129 -0.4450 0.06076  0.1365

## 2 levels for each response , fixed weights 
datat$response.f <- as.factor(datat$response)
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),
        estpr=c(0,0),pi0=0.5,pi1=0.5)
bb
#> Simple estimator :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.5195752 0.18171540
#> A0.f=1, response.f*A1.f=2,1 0.5264182 0.18212146
#> A0.f=1, response.f*A1.f=1,2 0.6343225 0.09083112
#> A0.f=1, response.f*A1.f=2,2 0.6411655 0.08810024
#> A0.f=2, response.f*A1.f=1,1 0.3023142 0.11081204
#> A0.f=2, response.f*A1.f=2,1 0.3199096 0.09719853
#> A0.f=2, response.f*A1.f=1,2 0.5294417 0.13399244
#> A0.f=2, response.f*A1.f=2,2 0.5470372 0.13251846
#> 
#> First Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.6359737 0.21658343
#> A0.f=1, response.f*A1.f=2,1 0.6425345 0.21728891
#> A0.f=1, response.f*A1.f=1,2 0.7129831 0.06901176
#> A0.f=1, response.f*A1.f=2,2 0.7195438 0.06513763
#> A0.f=2, response.f*A1.f=1,1 0.2561355 0.12249210
#> A0.f=2, response.f*A1.f=2,1 0.2790365 0.10488801
#> A0.f=2, response.f*A1.f=1,2 0.4557345 0.14361723
#> A0.f=2, response.f*A1.f=2,2 0.4786356 0.14226926
#> 
#> Second Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.4046498 0.19742912
#> A0.f=1, response.f*A1.f=2,1 0.5687969 0.23376677
#> A0.f=1, response.f*A1.f=1,2 0.5910450 0.09523643
#> A0.f=1, response.f*A1.f=2,2 0.6498579 0.08872953
#> A0.f=2, response.f*A1.f=1,1 0.3039991 0.10638060
#> A0.f=2, response.f*A1.f=2,1 0.3311109 0.09138384
#> A0.f=2, response.f*A1.f=1,2 0.5569428 0.11332978
#> A0.f=2, response.f*A1.f=2,2 0.5164913 0.12796725
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.5212352 0.21098387
#> A0.f=1, response.f*A1.f=2,1 0.6994484 0.26830813
#> A0.f=1, response.f*A1.f=1,2 0.6692919 0.07617047
#> A0.f=1, response.f*A1.f=2,2 0.7328127 0.06615399
#> A0.f=2, response.f*A1.f=1,1 0.2586853 0.11531506
#> A0.f=2, response.f*A1.f=2,1 0.2935352 0.09702298
#> A0.f=2, response.f*A1.f=1,2 0.4809089 0.11457277
#> A0.f=2, response.f*A1.f=2,2 0.4602799 0.13349781

## 2 levels for each response ,  estimated treat probabilities
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),estpr=c(1,1))
bb
#> Simple estimator :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.5733301 0.25088070
#> A0.f=1, response.f*A1.f=2,1 0.6022300 0.25101611
#> A0.f=1, response.f*A1.f=1,2 0.6893786 0.07700325
#> A0.f=1, response.f*A1.f=2,2 0.7182785 0.08178121
#> A0.f=2, response.f*A1.f=1,1 0.2851197 0.09997901
#> A0.f=2, response.f*A1.f=2,1 0.2841367 0.07893846
#> A0.f=2, response.f*A1.f=1,2 0.4821020 0.10343782
#> A0.f=2, response.f*A1.f=2,2 0.4811190 0.10002228
#> 
#> First Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.5934059 0.27131225
#> A0.f=1, response.f*A1.f=2,1 0.6224367 0.27204244
#> A0.f=1, response.f*A1.f=1,2 0.6962560 0.07772829
#> A0.f=1, response.f*A1.f=2,2 0.7252868 0.08319005
#> A0.f=2, response.f*A1.f=1,1 0.2738115 0.10244557
#> A0.f=2, response.f*A1.f=2,1 0.2767634 0.08081569
#> A0.f=2, response.f*A1.f=1,2 0.4661741 0.10481333
#> A0.f=2, response.f*A1.f=2,2 0.4691260 0.10238718
#> 
#> Second Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.4372672 0.23706174
#> A0.f=1, response.f*A1.f=2,1 0.5854429 0.26329507
#> A0.f=1, response.f*A1.f=1,2 0.6685369 0.07730893
#> A0.f=1, response.f*A1.f=2,2 0.7320359 0.08145477
#> A0.f=2, response.f*A1.f=1,1 0.2745442 0.10426421
#> A0.f=2, response.f*A1.f=2,1 0.2988823 0.07578047
#> A0.f=2, response.f*A1.f=1,2 0.5019477 0.09984288
#> A0.f=2, response.f*A1.f=2,2 0.4745778 0.10218550
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                  coef           
#> A0.f=1, response.f*A1.f=1,1 0.4702327 0.25293879
#> A0.f=1, response.f*A1.f=2,1 0.6127080 0.28587900
#> A0.f=1, response.f*A1.f=1,2 0.6759149 0.07701506
#> A0.f=1, response.f*A1.f=2,2 0.7433289 0.08370622
#> A0.f=2, response.f*A1.f=1,1 0.2634297 0.10565890
#> A0.f=2, response.f*A1.f=2,1 0.2948997 0.07656016
#> A0.f=2, response.f*A1.f=1,2 0.4865367 0.09860797
#> A0.f=2, response.f*A1.f=2,2 0.4699980 0.10193373


## 2 and 3 levels for each response , fixed weights 
datat$A1.23.f <- as.numeric(datat$A1.f)
dtable(datat,~A1.23.f+response)
#> 
#>         response   0   1
#> A1.23.f                 
#> 1                120  23
#> 2                120  25
datat <- dtransform(datat,A1.23.f=2+rbinom(nrow(datat),1,0.5),
            Count2==1 & A1.23.f==2 & response==0)
dtable(datat,~A1.23.f+response)
#> 
#>         response   0   1
#> A1.23.f                 
#> 1                120  23
#> 2                111  25
#> 3                  9   0
datat$A1.23.f <- as.factor(datat$A1.23.f)
dtable(datat,~A1.23.f+response|Count2==1)
#> 
#>         response  0  1
#> A1.23.f               
#> 1                21 23
#> 2                10 25
#> 3                 9  0
###
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.23.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),
        estpr=c(1,0),pi1=c(0.3,0.5))
bb
#> Simple estimator :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.5928568 0.20464868
#> A0.f=1, response.f*A1.23.f=2,1 0.6055291 0.20241441
#> A0.f=1, response.f*A1.23.f=3,1 0.5539792 0.19865003
#> A0.f=1, response.f*A1.23.f=1,2 0.7203538 0.09728960
#> A0.f=1, response.f*A1.23.f=2,2 0.7330260 0.08618558
#> A0.f=1, response.f*A1.23.f=3,2 0.6814762 0.07905734
#> A0.f=2, response.f*A1.23.f=1,1 0.3487105 0.14756981
#> A0.f=2, response.f*A1.23.f=2,1 0.2724117 0.09182351
#> A0.f=2, response.f*A1.23.f=3,1 0.2669705 0.10319223
#> A0.f=2, response.f*A1.23.f=1,2 0.5551901 0.15399346
#> A0.f=2, response.f*A1.23.f=2,2 0.4788913 0.12263546
#> A0.f=2, response.f*A1.23.f=3,2 0.4734501 0.12853150
#> 
#> First Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.6091794 0.22507560
#> A0.f=1, response.f*A1.23.f=2,1 0.6246762 0.22364858
#> A0.f=1, response.f*A1.23.f=3,1 0.5756603 0.21982886
#> A0.f=1, response.f*A1.23.f=1,2 0.7242102 0.10066515
#> A0.f=1, response.f*A1.23.f=2,2 0.7397070 0.08813234
#> A0.f=1, response.f*A1.23.f=3,2 0.6906911 0.07986256
#> A0.f=2, response.f*A1.23.f=1,1 0.3348012 0.15239160
#> A0.f=2, response.f*A1.23.f=2,1 0.2718558 0.09060009
#> A0.f=2, response.f*A1.23.f=3,1 0.2532931 0.10673462
#> A0.f=2, response.f*A1.23.f=1,2 0.5362379 0.15754640
#> A0.f=2, response.f*A1.23.f=2,2 0.4732925 0.12378699
#> A0.f=2, response.f*A1.23.f=3,2 0.4547298 0.13192021
#> 
#> Second Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.4707095 0.21804098
#> A0.f=1, response.f*A1.23.f=2,1 0.6287053 0.22907037
#> A0.f=1, response.f*A1.23.f=3,1 0.6778953 0.30311581
#> A0.f=1, response.f*A1.23.f=1,2 0.6499002 0.12488660
#> A0.f=1, response.f*A1.23.f=2,2 0.7656237 0.07140976
#> A0.f=1, response.f*A1.23.f=3,2 0.6875010 0.08312068
#> A0.f=2, response.f*A1.23.f=1,1 0.2656521 0.17396985
#> A0.f=2, response.f*A1.23.f=2,1 0.2706333 0.08715854
#> A0.f=2, response.f*A1.23.f=3,1 0.3235400 0.07994612
#> A0.f=2, response.f*A1.23.f=1,2 0.4759907 0.16071698
#> A0.f=2, response.f*A1.23.f=2,2 0.4671860 0.11714753
#> A0.f=2, response.f*A1.23.f=3,2 0.5050170 0.11067832
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.5068915 0.22640669
#> A0.f=1, response.f*A1.23.f=2,1 0.6601925 0.25328437
#> A0.f=1, response.f*A1.23.f=3,1 0.7128045 0.32561624
#> A0.f=1, response.f*A1.23.f=1,2 0.6681178 0.12092323
#> A0.f=1, response.f*A1.23.f=2,2 0.7709766 0.07310528
#> A0.f=1, response.f*A1.23.f=3,2 0.7088981 0.08171051
#> A0.f=2, response.f*A1.23.f=1,1 0.2472033 0.17493179
#> A0.f=2, response.f*A1.23.f=2,1 0.2703258 0.08650158
#> A0.f=2, response.f*A1.23.f=3,1 0.3183141 0.08043082
#> A0.f=2, response.f*A1.23.f=1,2 0.4540746 0.15704473
#> A0.f=2, response.f*A1.23.f=2,2 0.4667557 0.11617870
#> A0.f=2, response.f*A1.23.f=3,2 0.4960792 0.11127903

## 2 and 3 levels for each response , estimated 
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.23.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),estpr=c(1,1))
bb
#> Simple estimator :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.5733301 0.25088123
#> A0.f=1, response.f*A1.23.f=2,1 0.6486249 0.25863637
#> A0.f=1, response.f*A1.23.f=3,1 0.5558352 0.25058134
#> A0.f=1, response.f*A1.23.f=1,2 0.6893788 0.07700333
#> A0.f=1, response.f*A1.23.f=2,2 0.7646736 0.11893993
#> A0.f=1, response.f*A1.23.f=3,2 0.6718839 0.07549715
#> A0.f=2, response.f*A1.23.f=1,1 0.2851198 0.09997906
#> A0.f=2, response.f*A1.23.f=2,1 0.2795959 0.10086233
#> A0.f=2, response.f*A1.23.f=3,1 0.2893264 0.12751179
#> A0.f=2, response.f*A1.23.f=1,2 0.4821024 0.10343788
#> A0.f=2, response.f*A1.23.f=2,2 0.4765785 0.12129977
#> A0.f=2, response.f*A1.23.f=3,2 0.4863090 0.13928327
#> 
#> First Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.5934060 0.27131280
#> A0.f=1, response.f*A1.23.f=2,1 0.6665511 0.27965619
#> A0.f=1, response.f*A1.23.f=3,1 0.5783224 0.27132992
#> A0.f=1, response.f*A1.23.f=1,2 0.6962562 0.07772837
#> A0.f=1, response.f*A1.23.f=2,2 0.7694013 0.12078013
#> A0.f=1, response.f*A1.23.f=3,2 0.6811727 0.07593118
#> A0.f=2, response.f*A1.23.f=1,1 0.2738116 0.10244561
#> A0.f=2, response.f*A1.23.f=2,1 0.2791785 0.10064910
#> A0.f=2, response.f*A1.23.f=3,1 0.2740035 0.13235513
#> A0.f=2, response.f*A1.23.f=1,2 0.4661745 0.10481340
#> A0.f=2, response.f*A1.23.f=2,2 0.4715413 0.12263585
#> A0.f=2, response.f*A1.23.f=3,2 0.4663664 0.14396909
#> 
#> Second Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.4372664 0.23706211
#> A0.f=1, response.f*A1.23.f=2,1 0.5867572 0.24940274
#> A0.f=1, response.f*A1.23.f=3,1 0.6818858 0.34853309
#> A0.f=1, response.f*A1.23.f=1,2 0.6685368 0.07730905
#> A0.f=1, response.f*A1.23.f=2,2 0.7678408 0.07768404
#> A0.f=1, response.f*A1.23.f=3,2 0.6794316 0.07967452
#> A0.f=2, response.f*A1.23.f=1,1 0.2745441 0.10426433
#> A0.f=2, response.f*A1.23.f=2,1 0.2695053 0.09394894
#> A0.f=2, response.f*A1.23.f=3,1 0.3345432 0.09931154
#> A0.f=2, response.f*A1.23.f=1,2 0.5019478 0.09984303
#> A0.f=2, response.f*A1.23.f=2,2 0.4603587 0.11962056
#> A0.f=2, response.f*A1.23.f=3,2 0.5047271 0.12508846
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.23.f=1,1 0.4702319 0.25293915
#> A0.f=1, response.f*A1.23.f=2,1 0.6117617 0.26980849
#> A0.f=1, response.f*A1.23.f=3,1 0.7132649 0.36786766
#> A0.f=1, response.f*A1.23.f=1,2 0.6759149 0.07701519
#> A0.f=1, response.f*A1.23.f=2,2 0.7733904 0.07957475
#> A0.f=1, response.f*A1.23.f=3,2 0.7066309 0.07677686
#> A0.f=2, response.f*A1.23.f=1,1 0.2634296 0.10565903
#> A0.f=2, response.f*A1.23.f=2,1 0.2691166 0.09330578
#> A0.f=2, response.f*A1.23.f=3,1 0.3296791 0.10056744
#> A0.f=2, response.f*A1.23.f=1,2 0.4865368 0.09860813
#> A0.f=2, response.f*A1.23.f=2,2 0.4605674 0.11777635
#> A0.f=2, response.f*A1.23.f=3,2 0.4971161 0.12478451

## 2 and 1 level for each response 
datat$A1.21.f <- as.numeric(datat$A1.f)
dtable(datat,~A1.21.f+response|Count2==1)
#> 
#>         response  0  1
#> A1.21.f               
#> 1                21 23
#> 2                19 25
datat <- dtransform(datat,A1.21.f=1,Count2==1 & response==1)
dtable(datat,~A1.21.f+response|Count2==1)
#> 
#>         response  0  1
#> A1.21.f               
#> 1                21 48
#> 2                19  0
datat$A1.21.f <- as.factor(datat$A1.21.f)
dtable(datat,~A1.21.f+response|Count2==1)
#> 
#>         response  0  1
#> A1.21.f               
#> 1                21 48
#> 2                19  0
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.21.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),estpr=c(1,1))
bb
#> Simple estimator :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6352226 0.11813045
#> A0.f=1, response.f*A1.21.f=2,1 0.6641226 0.12002926
#> A0.f=2, response.f*A1.21.f=1,1 0.3865954 0.09118205
#> A0.f=2, response.f*A1.21.f=2,1 0.3856124 0.07700508
#> 
#> First Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6482593 0.12794220
#> A0.f=1, response.f*A1.21.f=2,1 0.6772901 0.13063539
#> A0.f=2, response.f*A1.21.f=1,1 0.3729074 0.09195877
#> A0.f=2, response.f*A1.21.f=2,1 0.3758593 0.07808807
#> 
#> Second Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6435175 0.11802262
#> A0.f=1, response.f*A1.21.f=2,1 0.6882148 0.11332360
#> A0.f=2, response.f*A1.21.f=1,1 0.3954927 0.09148962
#> A0.f=2, response.f*A1.21.f=2,1 0.3782504 0.08450470
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6685224 0.13033444
#> A0.f=1, response.f*A1.21.f=2,1 0.7138566 0.12655030
#> A0.f=2, response.f*A1.21.f=1,1 0.3839378 0.08986106
#> A0.f=2, response.f*A1.21.f=2,1 0.3758166 0.08434474

## known weights 
bb <- binregTSR(Event(entry,time,status)~+1+cluster(id),datat,time=2,cause=c(1),response.code=2,
        treat.model0=A0.f~+1, treat.model1=A1.21.f~A0.f*response.f,
        augmentR0=~X01+X02, augmentR1=~X11+X12,
        augmentC=~X01+X02+A11t+A12t+X11+X12+TR, cens.model=~strata(A0.f),estpr=c(1,0),pi1=c(0.5,1))
bb
#> Simple estimator :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6410543 0.12005600
#> A0.f=1, response.f*A1.21.f=2,1 0.6486576 0.11864403
#> A0.f=2, response.f*A1.21.f=1,1 0.3780709 0.09171015
#> A0.f=2, response.f*A1.21.f=2,1 0.3940667 0.08377301
#> 
#> First Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6532872 0.12997608
#> A0.f=1, response.f*A1.21.f=2,1 0.6625853 0.12899925
#> A0.f=2, response.f*A1.21.f=1,1 0.3647406 0.09331039
#> A0.f=2, response.f*A1.21.f=2,1 0.3842369 0.08422015
#> 
#> Second Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6435110 0.12122751
#> A0.f=1, response.f*A1.21.f=2,1 0.6751763 0.11432293
#> A0.f=2, response.f*A1.21.f=1,1 0.3957743 0.08413935
#> A0.f=2, response.f*A1.21.f=2,1 0.3772145 0.09110553
#> 
#> 1st and 2nd Randomization Augmentation :
#>                                     coef           
#> A0.f=1, response.f*A1.21.f=1,1 0.6709750 0.13165583
#> A0.f=1, response.f*A1.21.f=2,1 0.7043078 0.12732297
#> A0.f=2, response.f*A1.21.f=1,1 0.3861201 0.08279377
#> A0.f=2, response.f*A1.21.f=2,1 0.3757694 0.09080739

Two-Stage Randomization CALGB-9823

We here illustrate some analysis of one SMART conducted by Cancer and Leukemia Group B Protocol 8923 (CALGB 8923), Stone and others (2001). 388 patients were randomized to an initial treatment of GM-CSF (A1 ) or standard chemotherapy (A2 ). Patients with complete remission and informed consent to second stage were then re-randomized to only cytarabine (B1 ) or cytarabine plus mitoxantrone (B2 ).

We first compute the weighted risk-set estimator based on estimated weights \[\begin{align*} \Lambda_{A1,B1}(t) & = \sum_i \int_0^t \frac{w_i(s)}{Y^w(s)} dN_i(s) \end{align*}\] where \(w_i(s) = I(A0_i=A1) + (t>T_R) I(A1_i=B1)/\pi_1(X_i)\), that is 1 when you start on treatment \(A1\) and then for those that changes to \(B1\) at time \(T_R\) then is scaled up with the proportion doing this. This is equivalent to the IPTW (inverse probability of treatment weighted estimator). We estimate the treatment regimes \(A1, B1\) and \(A2, B1\) by letting \(A10\) indicate those that are consistent with ending on \(B1\), and similary, we estimate the treatment regimes \(A1, B2\) and \(A2, B2\), by letting \(A11\) indicate those that are consistent with ending on \(B2\). Note that for example \(A10\) then starts being \(1\) and becomes \(0\) if the subject is treated with \(B2\), but stays \(1\) if the subject is treated with \(B1\).

the treatment models are for all time-points, unless the weight.var variable is given (1 for treatments, 0 otherwise) to accomodate a general start,stop format
the treatment model may also depend on a response value
standard errors are based on influence functions and is also computed for the baseline

We here use the simple propensity score model \(P(A1=B1|A0_i)\) that uses the observed frequencies on arm \(B1\) among those starting out on either \(A1\) or \(A2\).

data(calgb8923)
calgt <- calgb8923

tm=At.f~factor(Count2)+age+sex+wbc
tm=At.f~factor(Count2)
ll0 <- phreg_IPTW(Event(start,time,status==1)~strata(A0,A10)+cluster(id),calgt,treat.model=tm)
pll0 <- predict(ll0,expand.grid(A0=0:1,A10=0,id=1))
ll1 <- phreg_IPTW(Event(start,time,status==1)~strata(A0,A11)+cluster(id),calgt,treat.model=tm)
pll1 <- predict(ll1,expand.grid(A0=0:1,A11=1,id=1))
plot(pll0,se=1,lwd=2,col=1:2,lty=1,xlab="time (months)")
plot(pll1,add=TRUE,col=3:4,se=1,lwd=2,lty=1)
legend("topright",c("A1B1","A2B1","A1B2","A2B2"),col=c(1,2,3,4),lty=1)

I here use the simple propensity score mode, but increased efficiency should be acheieved by extending the model, but here does not give much.

SessionInfo

sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: aarch64-apple-darwin24.2.0
#> Running under: macOS Sequoia 15.2
#> 
#> Matrix products: default
#> BLAS:   /Users/klaus/.asdf/installs/R/4.4.2/lib/R/lib/libRblas.dylib 
#> LAPACK: /Users/klaus/.asdf/installs/R/4.4.2/lib/R/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Copenhagen
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mets_1.3.5     timereg_2.0.6  survival_3.8-3
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.3           knitr_1.49          rlang_1.1.4        
#>  [4] xfun_0.50           jsonlite_1.8.9      listenv_0.9.1      
#>  [7] future.apply_1.11.3 lava_1.8.1          htmltools_0.5.8.1  
#> [10] sass_0.4.9          rmarkdown_2.29      grid_4.4.2         
#> [13] evaluate_1.0.1      jquerylib_0.1.4     fastmap_1.2.0      
#> [16] mvtnorm_1.3-2       yaml_2.3.10         lifecycle_1.0.4    
#> [19] numDeriv_2016.8-1.1 compiler_4.4.2      codetools_0.2-20   
#> [22] ucminf_1.2.2        Rcpp_1.0.13-1       future_1.34.0      
#> [25] lattice_0.22-6      digest_0.6.37       R6_2.5.1           
#> [28] parallelly_1.41.0   parallel_4.4.2      splines_4.4.2      
#> [31] bslib_0.8.0         Matrix_1.7-1        tools_4.4.2        
#> [34] globals_0.16.3      cachem_1.1.0

Two-Stage Randomization for for Competing risks and Survival outcomes

Klaus Holst & Thomas Scheike

2025-01-11

Two-Stage Randomization for for Competing risks and Survival outcomes

Two-Stage Randomization CALGB-9823

SessionInfo