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Type 'q()' to quit R. > library(testthat) > library(concrete) > library(data.table) > > test_check("concrete") Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Time (n at risk): 2500 (123/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 These candidate learners are not in the base SuperLearner package: SL.glmnet Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Time (n at risk): 2500 (123/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 1 candidate - SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 Getting Initial Estimates: Trt: Loading required package: nnls Loading required namespace: glmnet Done Hazards: Done Trt Time Event ratio 1: A=1 2500 2 9.45 2: A=0 2500 2 7.23 3: A=1 2500 -1 7.10 Norm PnEIC = 0.07435979 Starting TMLE Update: Starting step 1 with update epsilon = 0.1 Trt Time Event ratio 1: A=1 2500 1 2.51 2: A=1 2500 2 2.39 3: A=0 2500 -1 2.23 Norm PnEIC = 0.02082096 Starting step 2 with update epsilon = 0.1 Update increased ||PnEIC||, halving OneStepEps Starting step 2 with update epsilon = 0.05 Trt Time Event ratio 1: A=1 2500 -1 1.49 2: A=1 2500 1 1.12 3: A=1 2500 2 1.02 Norm PnEIC = 0.008429496 Starting step 3 with update epsilon = 0.05 Update increased ||PnEIC||, halving OneStepEps Starting step 3 with update epsilon = 0.025 Trt Time Event ratio 1: A=1 2500 1 0.90 2: A=1 2500 2 0.65 3: A=0 2500 -1 0.50 Norm PnEIC = 0.005525515 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Events: 1, 2 | Target Time: 2500 TMLE converged at step 4 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.glmnet_All 0.2514986 1 Cens. 0: Risk Coef TrtOnly 443.1608 1 MainTerms 443.9059 0 Event 1: Risk Coef TrtOnly 61.66929 0 MainTerms 53.51923 1 Event 2: Risk Coef TrtOnly 400.0922 0 MainTerms 390.1409 1 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Events: 1, 2 | Target Time: 2500 TMLE converged at step 4 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.glmnet_All 0.2514986 1 Cens. 0: Risk Coef TrtOnly 443.1608 1 MainTerms 443.9059 0 Event 1: Risk Coef TrtOnly 61.66929 0 MainTerms 53.51923 1 Event 2: Risk Coef TrtOnly 400.0922 0 MainTerms 390.1409 1 These candidate learners are not in the base SuperLearner package: SL.glmnet Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Time (n at risk): 2500 (123/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 1 candidate - SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 1, starting one-step epsilon = 0.1 Getting Initial Estimates: Trt: Done Hazards: Done Trt Time Event ratio 1: A=1 2500 2 9.45 2: A=0 2500 2 7.23 3: A=1 2500 -1 7.10 Norm PnEIC = 0.07435979 Starting TMLE Update: Starting step 1 with update epsilon = 0.1 Trt Time Event ratio 1: A=1 2500 1 2.51 2: A=1 2500 2 2.39 3: A=0 2500 -1 2.23 Norm PnEIC = 0.02082096 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Events: 1, 2 | Target Time: 2500 **TMLE did not converge!!** Intervention Time Event Pt Est se PnEIC abs(PnEIC / Stop Criteria) 1: A=1 2500 1 0.07143 0.01747 -0.007254 2.506 2: A=1 2500 2 0.33241 0.03729 0.014738 2.386 3: A=0 2500 -1 0.58627 0.04291 -0.014662 2.230 4: A=0 2500 2 0.33354 0.03645 0.012631 2.091 5: A=1 2500 -1 0.59616 0.04118 -0.007484 1.168 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.glmnet_All 0.251687 1 Cens. 0: Risk Coef TrtOnly 443.1608 1 MainTerms 443.9059 0 Event 1: Risk Coef TrtOnly 61.66929 0 MainTerms 53.51923 1 Event 2: Risk Coef TrtOnly 400.0922 0 MainTerms 390.1409 1 Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=186 (0.44), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Event: 1 Target Time (n at risk): 2500 (123/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 2, starting one-step epsilon = 0.1 Getting Initial Estimates: Trt: Loading required namespace: xgboost Done Hazards: Done Trt Time Event ratio 1: A=0 2500 1 12.01 2: A=0 2500 -1 12.01 3: A=1 2500 1 11.05 Norm PnEIC = 0.10096 Starting TMLE Update: Starting step 1 with update epsilon = 0.1 Trt Time Event ratio 1: A=0 2500 1 5.56 2: A=0 2500 -1 5.56 3: A=1 2500 1 5.09 Norm PnEIC = 0.04638232 Starting step 2 with update epsilon = 0.1 Trt Time Event ratio 1: A=0 2500 1 1.38 2: A=0 2500 -1 1.38 3: A=1 2500 1 1.25 Norm PnEIC = 0.01144192 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Event: 1 | Target Time: 2500 **TMLE did not converge!!** Intervention Time Event Pt Est se PnEIC abs(PnEIC / Stop Criteria) 1: A=0 2500 -1 0.5894 0.03746 0.008551 1.378 2: A=0 2500 1 0.4106 0.03746 -0.008551 1.378 3: A=1 2500 -1 0.6116 0.03660 0.007603 1.254 4: A=1 2500 1 0.3884 0.03660 -0.007603 1.254 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.xgboost_All 0.2690687 0.2956436 SL.glmnet_All 0.2514009 0.7043564 Cens. 0: Risk Coef TrtOnly 446.9140 1 MainTerms 447.2758 0 Event 1: Risk Coef TrtOnly 456.5961 0 MainTerms 454.3996 1 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Event: 1 | Target Time: 2500 **TMLE did not converge!!** Intervention Time Event Pt Est se PnEIC abs(PnEIC / Stop Criteria) 1: A=0 2500 -1 0.5894 0.03746 0.008551 1.378 2: A=0 2500 1 0.4106 0.03746 -0.008551 1.378 3: A=1 2500 -1 0.6116 0.03660 0.007603 1.254 4: A=1 2500 1 0.3884 0.03660 -0.007603 1.254 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.xgboost_All 0.2690687 0.2956436 SL.glmnet_All 0.2514009 0.7043564 Cens. 0: Risk Coef TrtOnly 446.9140 1 MainTerms 447.2758 0 Event 1: Risk Coef TrtOnly 456.5961 0 MainTerms 454.3996 1 Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Times (n at risk): 606.8 (376/418), 789.1 (355/418), 974.8 (334/418), ..., 2851.8 (84/418), 3162.85 (63/418), 3524.2 (42/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 These candidate learners are not in the base SuperLearner package: SL.xgboost,SL.glmnet Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Times (n at risk): 606.8 (376/418), 789.1 (355/418), 974.8 (334/418), ..., 2851.8 (84/418), 3162.85 (63/418), 3524.2 (42/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 These candidate learners are not in the base SuperLearner package: SL.xgboost,SL.glmnet Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Times (n at risk): 606.8 (376/418), 789.1 (355/418), 974.8 (334/418), ..., 2851.8 (84/418), 3162.85 (63/418), 3524.2 (42/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 Error in (function (x) : unused argument (list(c(58.7652292950034, 56.4462696783025, 70.072553045859, 54.7405886379192, 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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, Error in (function (x) : unused argument (list(c(58.7652292950034, 56.4462696783025, 70.072553045859, 54.7405886379192, 38.1054072553046, 66.258726899384, 55.5345653661875, 53.056810403833, 42.507871321013, 70.5598904859685, 53.7138945927447, 59.1375770020534, 45.6892539356605, 56.2217659137577, 64.6461327857632, 40.4435318275154, 52.1834360027378, 53.9301848049281, 49.5605749486653, 59.9534565366188, 64.1889117043121, 56.2765229295003, 55.9671457905544, 44.5201916495551, 45.0732375085558, 52.0246406570842, 54.4394250513347, 44.9472963723477, 63.8767967145791, 41.3853524982888, 41.5523613963039, 53.9958932238193, 51.2826830937714, 52.0602327173169, 48.6187542778919, 56.4106776180698, 61.7275838466804, 36.6269678302532, 55.3921971252567, 46.6694045174538, 33.6344969199179, 33.6947296372348, 48.870636550308, 37.5824777549624, 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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, Argument 'Verbose' must be either TRUE or FALSE, so has been set to FALSE by default Argument 'GComp' must be either TRUE or FALSE, so has been set to FALSE by default Argument 'ReturnModels' must be either TRUE or FALSE, so has been set to FALSE by default Argument 'RenameCovs' must be either TRUE or FALSE, so has been set to FALSE by default Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Time (n at risk): 1917.78229665072 (188/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=25 (0.06), [min,max] = [533, 3092] Event 2 : n=161 (0.39), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: age age . 2: sex sex . - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Events: 1, 2 Target Time (n at risk): 1917.78229665072 (188/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 2 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 Observed Data (418 rows x 6 cols) Unique IDs: "id" (n=418), Time-to-Event: "time", Event Type: "status", Treatment: "trt" Events: Cens. 0 : n=232 (0.56), [min,max] = [691, 4795] Event 1 : n=186 (0.44), [min,max] = [41, 4191] 1 Treatment Variable : trt : 0: n=212 (0.51) 1: n=206 (0.49) 2 Baseline Covariates ColName CovName CovVal 1: L1 age . 2: L2 sex f - - - - - - - - - - - - - - - - - - - - Estimand Specification: Target Event: 1 Target Time (n at risk): 2500 (123/418) Interventions A=1: ("trt" = [1,1,1,1,1,1,1,1,1,1,...]) - Observed Prevalence = 0.49 A=0: ("trt" = [0,0,0,0,0,0,0,0,0,0,...]) - Observed Prevalence = 0.51 - - - - - - - - - - - - - - - - - - - - Estimation Specification: Stratified 20-Fold Cross Validation "trt" Propensity Score Estimation (SuperLearner): Default SL Selector, Default Loss Fn, 2 candidates - SL.xgboost, SL.glmnet Cens. 0 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms Event 1 Estimation (coxph): Discrete SL Selector, Log Partial-LL Loss, 2 candidates - TrtOnly, MainTerms One-step TMLE (finite sum approx.) simultaneously targeting all cause-specific Absolute Risks g nuisance bounds = [0.04052, 1], max update steps = 500, starting one-step epsilon = 0.1 Getting Initial Estimates: Trt: Done Hazards: Done Trt Time Event ratio 1: A=0 2500 1 12.01 2: A=0 2500 -1 12.01 3: A=1 2500 1 11.05 Norm PnEIC = 0.10096 Starting TMLE Update: Starting step 1 with update epsilon = 0.1 Trt Time Event ratio 1: A=0 2500 1 5.56 2: A=0 2500 -1 5.56 3: A=1 2500 1 5.09 Norm PnEIC = 0.04638232 Starting step 2 with update epsilon = 0.1 Trt Time Event ratio 1: A=0 2500 1 1.38 2: A=0 2500 -1 1.38 3: A=1 2500 1 1.25 Norm PnEIC = 0.01144192 Starting step 3 with update epsilon = 0.1 Update increased ||PnEIC||, halving OneStepEps Starting step 3 with update epsilon = 0.05 Update increased ||PnEIC||, halving OneStepEps Starting step 3 with update epsilon = 0.025 Trt Time Event ratio 1: A=0 2500 1 0.35 2: A=0 2500 -1 0.35 3: A=1 2500 1 0.33 Norm PnEIC = 0.0029372 Continuous-Time One-Step TMLE targeting the Cause-Specific Absolute Risks for: Interventions: "A=1", "A=0" | Target Event: 1 | Target Time: 2500 TMLE converged at step 4 For Intervention "A=1", no subjects had G-related nuisance weights falling below 0.0405 For Intervention "A=0", no subjects had G-related nuisance weights falling below 0.0405 Initial Estimators: Treatment "trt" : Risk SL Weight SL.xgboost_All 0.2690687 0.2956436 SL.glmnet_All 0.2514009 0.7043564 Cens. 0: Risk Coef TrtOnly 446.9140 1 MainTerms 447.2758 0 Event 1: Risk Coef TrtOnly 456.5961 0 MainTerms 454.3996 1 [ FAIL 0 | WARN 1 | SKIP 0 | PASS 70 ] [ FAIL 0 | WARN 1 | SKIP 0 | PASS 70 ] > > proc.time() user system elapsed 43.64 3.23 46.85