DRMLSurv is an R package for two-stage survival analysis with censoring, matching-based imputation, counterfactual outcome construction, and machine-learning estimation of dynamic treatment rules.
Main features include:
- estimate propensity and prognostic (and censoring) scores with SuperLearner
- donor-based imputation of censored stage-1 and stage-2 survival times
- matched counterfactual outcome construction under alternative treatment paths
- policy learning with random forests and cross-validation
- obtaining optimized regimes and policy summary metrics
Example workflow
data("DATASET", package = "DRMLSurv")
dat <- DATASET
set.seed(123)
folds <- caret::createFolds(dat$txgroup1L.sd, k = 3)
test_idx <- folds[[1]]
train_idx <- setdiff(seq_len(nrow(dat)), test_idx)
train_data <- dat[train_idx, , drop = FALSE]
test_data <- dat[test_idx, , drop = FALSE]
trainmod = Drmatch(
data = train_data,
id.var = 'patientid',
eta2.var = 'eta2',
Y1.var = 'OS_time.1L',
Y2.var = 'OS_time.2L',
delta.var = 'deathInd.raw',
OY.var = 'OS_time',
A1.var = 'txgroup1L.sd',
A2.var = 'txgroup2L0.sd',
names.var1 = c("ageAt1L.sd","gender.sd","Albumin1st.sd","Lymphocyte1st.sd",
"ECOG1st0.sd","ECOG1st1.sd", 'firstLineStartTime.sd'),
names.var2 = c("ageAt1L.sd","OS_time.1L","Albumin2nd.sd",
"gender.sd","Lymphocyte2nd.sd","ECOG2nd0.sd",
"ECOG2nd1.sd", "txgroup1L.sd", 'firstLineStartTime.sd'),
cores = 4,
sl.seed = 1234,
A.SL.library = list(
"SL.ranger",
"SL.glm",
"SL.glmnet"),
Y.SL.library = c("LIB_COXlasso", "LIB_COXall", "LIB_COXen"
),
A.method = "method.NNloglik",
Y.method = "ibll",
plotps = FALSE,
ngrid = 5000,
param.tune = list(
LIB_COXlasso = list(lambda=seq(0.001, 0.25, length.out = 10)),
LIB_COXall = NULL,
LIB_COXen = list(alpha=seq(.1, .9, length.out = 10),
lambda=seq(0.001, 0.1, length.out = 10))
),
maxit = 10000,
runseed = 2025,
useds = TRUE,
modeltype = "ranger",
usecv = TRUE,
doublepg = TRUE,
model.pg = "cox",
superLearn = TRUE,
distance = 'mahalanobis',
method = 'nearest',
K = 3,
replacement = TRUE,
cap_months = 24
)
Scores: censoring scores (SL): 3.745 sec elapsed
Stage 2 matching: 1.96 sec elapsed
Scores: censoring scores (SL): 16.049 sec elapsed
Stage 1 matching: 13.972 sec elapsed
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Scores: treatment PS + pg0/pg1 (SL): 32.946 sec elapsed
Stage 2 DoubleScore: 32.947 sec elapsed
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Scores: treatment PS + pg0/pg1 (SL): 110.1 sec elapsed
Stage 1 DoubleScore: 110.101 sec elapsed
Scores: censoring scores (SL): 3.672 sec elapsed
Stage 2 matching: 1.765 sec elapsed
Scores: censoring scores (SL): 15.574 sec elapsed
Stage 1 matching: 14.149 sec elapsed
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Scores: treatment PS + pg0/pg1 (SL): 33.143 sec elapsed
Stage 2 DoubleScore: 33.144 sec elapsed
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Scores: treatment PS + pg0/pg1 (SL): 111.273 sec elapsed
Stage 1 DoubleScore: 111.275 sec elapsed
Best set of tuning parameters and metrics overall
ntree = 1000
mtry = 2
nodesize = 2
CCR = 0.557317248755605
OOB = 0.442692123836303
Score = 7.84107735199969
Best set of tuning parameters and metrics overall
ntree = 1000
mtry = 2
nodesize = 10
CCR = 0.546726935592095
OOB = 0.459250100944479
Score = 12.4968720062604
Score = 12.4992031699597
#pred = predict(trainmod, newdata = test_data)
res <- summary(trainmod, newdata = test_data)
res
Stage 1 Stage 2 Overall
Accuracy 0.53151101 0.45853659 0.20000000
MCC 0.07769275 -0.01458439 -0.01281208
Sensitivity 0.60979730 0.31147541 0.23973930
Specificity 0.46758621 0.67469880 0.74672252
PPV 0.48326640 0.58461538 0.23776803
NPV 0.59473684 0.40000000 0.74674813
F1 0.53920836 0.40641711 0.20188035