Point survival curve

This study validates longitudinal TMLE with survival_model="hazard" and targeting="one_step" on a held design. The design has one baseline treatment, held over every visit, and it reads one follow-up time and one event code per unit (TimeToEvent). One walk moves one set of discrete hazards. The walk solves the score equation of every horizon and regimen at once (Cai and van der Laan 2020, Section 5; Rytgaard and van der Laan 2024, Section 3.4). Every reported risk curve is then non-decreasing in floating point. The section hazard-based survival targeting states the estimator.

The primary law is the survival scenario of point-treatment survival. Every truth is an exact finite sum. Every efficiency bound is the exact standard deviation of the efficient influence function over the law’s support.

The comparator is R tmle3 at commit ed72f8a, which runs tmle_survival with a one-step whole-curve hazard fluctuation. The comparator runs are pending, so this study carries the label “Python property evidence, with comparator runs pending”. The runs are queued as Study queue row 21 in TRACKER.md. The comparison is not acceptance evidence for any claim until that row closes.

What was compared

setting	cleverly	R tmle3
status	run and published on this page	pending. No reference process ran, and equivalence.csv has no rows
datasets	1,600 samples of 2,000 rows of the survival scenario	the identical rows, once the runs exist
scenario	survival: five visits, the risks of both arms and their difference at every visit	the same
treatment model	an unpenalized logistic regression on W1 and W2	Lrnr_glm on W1 + W2
event and censoring hazards	an unpenalized logistic regression on W1, W2 and the arm at each node	Lrnr_stratified on the visit over Lrnr_glm, one fit for each visit
targeting	targeting="one_step", step_size=1e-3, stopped at a relative score of 1e-10	tmle3_Update_survival with fit_method="l2", maxit=100 and delta_epsilon=0.01, stopped when every score is at most 1/n in absolute value
curve	the risks of both arms and their difference, from one set of hazards	one tmle_survival specification for each arm with target_times = 1:5. The contrast is the difference of the two per-unit influence curves, and the risk is one minus the reported survival
intervals	pointwise 95% Wald from the influence curve	the same

The pending runs read under the default margins. The comparator is tmle3 at commit ed72f8a with sl3 at commit 0e8f236. It runs in its own image, which adds glmnet 5.0 to the tmle3 pins. The runner subclasses the updater to repair two defects of its fit_submodel, and it changes nothing else. The vector test fit_lambda == 1 becomes length(fit_lambda) == 1. The retry assigns lambda.min.ratio, so a retry refits a different path.

A replicate with an unsolved score and no nonzero step fails the run. A replicate with a nonzero step and an unsolved score stays in the paired rows. The run log counts it, and this page will report it.

The declared unmatched settings are the cause of any difference between the two sides.

unmatched setting	cleverly	tmle3
bounds	its shipped g_bounds	g at 0.025 and the hazards at 0.025 and 0.005
censoring convention	a unit is censored at node k before its event there can be seen	the censoring hazard is fitted on rows that include the failure row
step	the universal least favorable submodel at step_size = 1e-3	ridge-penalized steps of at most delta_epsilon in norm
stopping rule	a relative score of 1e-10	every score at most 1/n in absolute value

The censoring convention makes the cleverly standard error grow against the tmle3 one with the visit. The study module records about 2 percent at visit 5 in its smoke run. A paired row at a late visit can therefore read as superior on coverage and not as equivalent. Both outcomes pass.

Accuracy against known truth

law	estimand	what was tested	implementation	bias (99% interval)	coverage	SE ratio	result
five visits, a binary baseline treatment held over every node, visit dropout	ate_regimen[arm1 vs arm0 @ t=1]	difference in cumulative risk between the plans “assign arm 1 at baseline and hold it” against “assign arm 0 at baseline and hold it” at horizon t = 1	cleverly	-0.000779 to 0.0011	0.9500	1.0029	pass
five visits, a binary baseline treatment held over every node, visit dropout	ate_regimen[arm1 vs arm0 @ t=2]	difference in cumulative risk between the plans “assign arm 1 at baseline and hold it” against “assign arm 0 at baseline and hold it” at horizon t = 2	cleverly	-0.000939 to 0.0015	0.9519	1.0039	pass
five visits, a binary baseline treatment held over every node, visit dropout	ate_regimen[arm1 vs arm0 @ t=3]	difference in cumulative risk between the plans “assign arm 1 at baseline and hold it” against “assign arm 0 at baseline and hold it” at horizon t = 3	cleverly	-0.000924 to 0.0018	0.9531	1.0124	pass
five visits, a binary baseline treatment held over every node, visit dropout	ate_regimen[arm1 vs arm0 @ t=4]	difference in cumulative risk between the plans “assign arm 1 at baseline and hold it” against “assign arm 0 at baseline and hold it” at horizon t = 4	cleverly	-0.000847 to 0.0021	0.9569	1.0364	pass
five visits, a binary baseline treatment held over every node, visit dropout	ate_regimen[arm1 vs arm0 @ t=5]	difference in cumulative risk between the plans “assign arm 1 at baseline and hold it” against “assign arm 0 at baseline and hold it” at horizon t = 5	cleverly	-0.0013 to 0.0018	0.9544	1.0084	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm0 @ t=1]	cumulative risk under the plan assign arm 0 at baseline and hold it at horizon t = 1	cleverly	-0.000838 to 0.000594	0.9519	0.9963	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm0 @ t=2]	cumulative risk under the plan assign arm 0 at baseline and hold it at horizon t = 2	cleverly	-0.000932 to 0.000929	0.9575	0.9958	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm0 @ t=3]	cumulative risk under the plan assign arm 0 at baseline and hold it at horizon t = 3	cleverly	-0.0011 to 0.0010	0.9481	0.9839	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm0 @ t=4]	cumulative risk under the plan assign arm 0 at baseline and hold it at horizon t = 4	cleverly	-0.0016 to 0.000649	0.9500	0.9851	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm0 @ t=5]	cumulative risk under the plan assign arm 0 at baseline and hold it at horizon t = 5	cleverly	-0.0017 to 0.000473	0.9494	0.9830	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm1 @ t=1]	cumulative risk under the plan assign arm 1 at baseline and hold it at horizon t = 1	cleverly	-0.000566 to 0.000609	0.9425	1.0035	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm1 @ t=2]	cumulative risk under the plan assign arm 1 at baseline and hold it at horizon t = 2	cleverly	-0.000515 to 0.0011	0.9475	1.0152	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm1 @ t=3]	cumulative risk under the plan assign arm 1 at baseline and hold it at horizon t = 3	cleverly	-0.000492 to 0.0014	0.9537	1.0310	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm1 @ t=4]	cumulative risk under the plan assign arm 1 at baseline and hold it at horizon t = 4	cleverly	-0.000864 to 0.0012	0.9500	1.0363	pass
five visits, a binary baseline treatment held over every node, visit dropout	risk_regimen[arm1 @ t=5]	cumulative risk under the plan assign arm 1 at baseline and hold it at horizon t = 5	cleverly	-0.0015 to 0.000754	0.9519	0.9936	pass

All 15 implementation-estimand tests pass. The tests are the five risks of each arm and the five differences, one for each visit. No replication raised an error, so no cell lost a replicate.

Theory properties

property	cell	role	what was tested	what must hold	measured	result
clustered_inference	clustered_t5__cluster_robust	positive	arm-0 risk at visit 5, 25 clusters of 80 that share W: the cluster-robust interval from the cluster-level influence curve of a single-fold hazard fit	SE-ratio and coverage intervals both stay inside their calibration bands	coverage 0.9373 to 0.9543, SE ratio 0.9660 to 1.0208, paired coverage gain 0.0875 to 0.1098	pass
clustered_inference	clustered_t5__iid_control	control	arm-0 risk at visit 5, 25 clusters of 80 that share W: the identical rows, point estimates, and influence curves treated as independent	the SE-ratio upper endpoint must not exceed the declared IID-control ceiling	coverage 0.8341 to 0.8610, SE ratio 0.7158 to 0.7546, paired coverage gain 0.0875 to 0.1098	pass
clustered_inference	clustered_t5_per_horizon__cluster_robust	positive	arm-0 risk at visit 5, 25 clusters of 80 that share W, targeted one horizon at a time: the cluster-robust interval from the cluster-level influence curve of a single-fold hazard fit	SE-ratio and coverage intervals both stay inside their calibration bands	coverage 0.9413 to 0.9578, SE ratio 0.9595 to 1.0141, paired coverage gain 0.0906 to 0.1140	pass
clustered_inference	clustered_t5_per_horizon__iid_control	control	arm-0 risk at visit 5, 25 clusters of 80 that share W, targeted one horizon at a time: the identical rows, point estimates, and influence curves treated as independent	the SE-ratio upper endpoint must not exceed the declared IID-control ceiling	coverage 0.8348 to 0.8616, SE ratio 0.7114 to 0.7508, paired coverage gain 0.0906 to 0.1140	pass
double_robustness	survival_t5__both_correct	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: both the outcome regression and the treatment mechanism are correctly specified	bias interval inside the equivalence margin, with the reported standard error on the scale of the empirical spread	bias -0.0026 to 0.0011, margin 0.0061, SE ratio 0.9974	pass
double_robustness	survival_t5__both_wrong	control	difference of arm 1 and arm 0 at visit 5, five-visit held law: both nuisances are misspecified	bias interval must fall entirely outside the margin, with the reported standard error still on the scale of the empirical spread	bias 0.0326 to 0.0363, margin 0.0061, SE ratio 0.9919	pass
double_robustness	survival_t5__mechanism_correct	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: only the treatment and censoring mechanisms are correctly specified	bias interval inside the equivalence margin, with the reported standard error on the scale of the empirical spread	bias -0.0026 to 0.0012, margin 0.0063, SE ratio 0.9738	pass
double_robustness	survival_t5__outcome_correct	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: only the outcome regression is correctly specified	bias interval inside the equivalence margin, with the reported standard error on the scale of the empirical spread	bias -0.0011 to 0.0025, margin 0.0061, SE ratio 0.9805	pass
interval_calibration	competing_t4__correctly_specified	positive	difference in the death incidence at visit 4, two causes: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9458 to 0.9616, SE ratio 0.9867 to 1.0373, empirical efficiency ratio 0.9641 to 1.0134, reported efficiency ratio 0.9991 to 1.0008	pass
interval_calibration	isotonic_t2__correctly_specified	positive	risk at visit 2 under treatment at both nodes, two-node law, fitted with the isotonic projection: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9240 to 0.9427, SE ratio 0.9391 to 0.9908, empirical efficiency ratio 0.9962 to 1.0500, reported efficiency ratio 0.9831 to 0.9905	pass
interval_calibration	policy_t3__correctly_specified	positive	difference of a known baseline policy and arm 0 at visit 3, three arms without L_t: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9400 to 0.9566, SE ratio 0.9666 to 1.0175, empirical efficiency ratio 0.9853 to 1.0373, reported efficiency ratio 1.0022 to 1.0031	pass
interval_calibration	rule_t3__correctly_specified	positive	difference of the rule that treats where W1 is one and arm 0 at visit 3, three arms without L_t: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9357 to 0.9529, SE ratio 0.9660 to 1.0168, empirical efficiency ratio 0.9858 to 1.0378, reported efficiency ratio 1.0017 to 1.0033	pass
interval_calibration	survival_t5__correctly_specified	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9439 to 0.9555, SE ratio 0.9817 to 1.0192, empirical efficiency ratio 0.9831 to 1.0205, reported efficiency ratio 1.0016 to 1.0022	pass
interval_calibration	survival_t5__noise_control	control	difference of arm 1 and arm 0 at visit 5, five-visit held law: one efficiency-bound unit of independent noise is added to each estimate	the empirical efficiency ratio must rise above the band	coverage 0.8263 to 0.8459, SE ratio 0.7009 to 0.7274, empirical efficiency ratio 1.3774 to 1.4295, reported efficiency ratio 1.0016 to 1.0022	pass
interval_calibration	survival_t5__shrunken_se_control	control	difference of arm 1 and arm 0 at visit 5, five-visit held law: the reported standard errors are multiplied by a declared factor below one	the SE-ratio interval must fall below the calibration band	coverage 0.8156 to 0.8357, SE ratio 0.6874 to 0.7135, empirical efficiency ratio 0.9830 to 1.0202, reported efficiency ratio 0.7011 to 0.7015	pass
interval_calibration	weighted_t5__correctly_specified	positive	difference at visit 5 under the weight 1 + W1 / 2: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9438 to 0.9599, SE ratio 0.9793 to 1.0298, empirical efficiency ratio 0.9727 to 1.0227, reported efficiency ratio 1.0012 to 1.0020	pass
interval_calibration	weighted_t5_per_horizon__correctly_specified	positive	difference at visit 5 under the weight 1 + W1 / 2, targeted one horizon at a time: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9373 to 0.9543, SE ratio 0.9694 to 1.0225, empirical efficiency ratio 0.9799 to 1.0335, reported efficiency ratio 1.0015 to 1.0024	pass
isotonic_projection	isotonic_t2__moved	diagnostic	risk at visit 2 under treatment at both nodes, two-node law, fitted with the isotonic projection: per replication, the distance the projection moved the level, against a truth of zero	none. The row is a diagnostic and states no verdict	binding share 0, 0 to 0.0011	reported
power	survival_t5__alternative	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: the same test applied to a law with a real effect	rejection lower bound clears the minimum power	rejection 1, 0.9934 to 1	pass
root_n_and_efficiency	survival_t5__n_1000	control	difference of arm 1 and arm 0 at visit 5, five-visit held law: bias, coverage and SE calibration at n = 1,000	coverage interval lies below nominal or clears the declared floor	bias 0.0022, coverage 0.9165 to 0.9606, SE ratio 1.0000	pass
root_n_and_efficiency	survival_t5__n_2000	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: bias, coverage and SE calibration at n = 2,000	bias inside the margin, coverage clears the floor, SE ratio inside the sanity band	bias 0.0013, coverage 0.9107 to 0.9565, SE ratio 0.9990	pass
root_n_and_efficiency	survival_t5__n_8000	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: bias, coverage and SE calibration at n = 8,000	bias inside the margin, coverage clears the floor, SE ratio inside the sanity band	bias 0.000172, coverage 0.9401 to 0.9767, SE ratio 1.0524	pass
root_n_rate	survival_t5__empirical_sd	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: log empirical spread of the estimates regressed on log n across three sizes	slope interval inside the root-n band and excluding -1/4	slope -0.5740 to -0.4845	pass
root_n_rate	survival_t5__reported_se	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: the same regression applied to the mean reported standard error	slope interval inside the root-n band and excluding -1/4	slope -0.5026 to -0.5013	pass
simultaneous_coverage	all_reported__pointwise_joint_control	control	every parameter the calibration fit reports: the pointwise 95% intervals of the same fits, read jointly	joint coverage upper endpoint must fall below the nominal rate	joint coverage 0.6469 to 0.6719	pass
simultaneous_coverage	all_reported__simultaneous_band	positive	every parameter the calibration fit reports: the max-t multiplier band over every estimand the law reports, from the same-row centered influence curves	joint coverage interval inside the calibration coverage band	joint coverage 0.9393 to 0.9513	pass
simultaneous_coverage	curve_arm0__pointwise_joint_control	control	the arm-0 risk curve alone, at the five visits: the pointwise 95% intervals of the same fits, read jointly	joint coverage upper endpoint must fall below the nominal rate	joint coverage 0.8247 to 0.8443	pass
simultaneous_coverage	curve_arm0__simultaneous_band	positive	the arm-0 risk curve alone, at the five visits: the max-t multiplier band over every estimand the law reports, from the same-row centered influence curves	joint coverage interval inside the calibration coverage band	joint coverage 0.9411 to 0.9530	pass
simultaneous_coverage	curve_arm1__pointwise_joint_control	control	the arm-1 risk curve alone, at the five visits: the pointwise 95% intervals of the same fits, read jointly	joint coverage upper endpoint must fall below the nominal rate	joint coverage 0.8284 to 0.8479	pass
simultaneous_coverage	curve_arm1__simultaneous_band	positive	the arm-1 risk curve alone, at the five visits: the max-t multiplier band over every estimand the law reports, from the same-row centered influence curves	joint coverage interval inside the calibration coverage band	joint coverage 0.9394 to 0.9514	pass
targeting_necessity	survival_t5__targeted	positive	difference of arm 1 and arm 0 at visit 5, five-visit held law: the estimator fluctuates a misspecified outcome model, so targeting does all the adjusting	bias interval inside the equivalence margin	bias -0.000485 to 0.0032, margin 0.0062	pass
targeting_necessity	survival_t5__untargeted	control	difference of arm 1 and arm 0 at visit 5, five-visit held law: the identical fit with every fluctuation step removed	bias interval must fall entirely outside the margin	bias 0.1405 to 0.1405, margin 0	pass

All 31 scored cells pass, and the isotonic diagnostic reports a binding share of zero. properties.csv has 32 rows, and failed_replicates is zero in all of them.

The cells use the same one-step fit as the primary study, except where the text says otherwise. The learners of every positive cell are correct. The hazard learner is an additive logistic fit on a one-cause law and the saturated cell fit on a two-cause law. The mechanism learners are the saturated cell fits.

family	positive cells	control that must fail
interval_calibration	the visit-5 difference on survival; the death incidence difference at visit 4 on competing; the rule and the known policy against arm 0 at visit 3 on a three-arm law; the weighted visit-5 difference, one step and per horizon; the visit-2 level of a two-node law after the isotonic projection	reported standard errors scaled by 0.7, and one efficiency-bound unit of added noise, on the one-step survival cell only
clustered_inference	the arm-0 risk at visit 5 on 25 clusters of 80 rows that share W, one step and per horizon	the same fit read with independent standard errors
double_robustness	the hazard correct, the mechanism correct, or both	both wrong
targeting_necessity	the targeted estimate with a wrong hazard and the correct mechanism	the plug-in of the same fit
root_n_and_efficiency	n = 2,000 and 8,000	n = 1,000
root_n_rate	the empirical spread and the reported standard error	none. The slope interval must exclude -0.25
power	the alternative on survival	none
simultaneous_coverage	the max-t band over each arm’s five-visit risk curve, and over the fifteen reported parameters	the pointwise intervals of the same fits, read jointly

The weighted_t5 cells use the weight 1 + W1 / 2. The rule_t3 cell uses the rule that treats where W1 is one. The policy_t3 cell uses a known baseline policy with probabilities (1/4, 1/4 + W1/4, 1/2 - W1/4) over three arms. Both read one fit of a three-arm law without L_t.

The wrong hazard learner is InterceptOnly from the property module of point-treatment survival. A saturated hazard fitted in sample is the maximum-likelihood estimate, so its initial score is zero and targeting does not move it. For that reason the necessity cell uses a wrong hazard. The untargeted control predicts one hazard for each node and both arms, so its plug-in contrast is zero in every sample.

The per-horizon calibration, weighted and clustered cells use targeting="iterative" with max_iter=100. They run the per-horizon algorithm with weights and with clusters. The primary study does not use it. The curve bands are the shipped multiplier band (simultaneous_bands, 2,000 rademacher draws, random_state=0) over the five risks of one arm. This is the band a fit of that one regimen reports.

The isotonic cell does not exercise a binding projection. The isotonic_t2 cell fits survival_model="iterated_mean" with isotonic=True on a law with a treatment at two nodes. Its curve rises from 0.375 at node 1 to 0.758 at node 2. That step is about nine standard errors at n = 2,000. The diagnostic isotonic_t2__moved therefore records that the projection moved the level in none of the 4,800 replications. The cell shows that the projection leaves the inference of a regular curve unchanged. It is not evidence for an interval after a projection that moves a level.

The property module gives the reason. The increment between two horizons is the event mass at the later node. The binding share is about \(\Phi(-\sqrt{m})\), with \(m\) the expected number of observed events there. A projection binds often only where a node has almost no events. There Westling, van der Laan and Carone (2020), Theorem 2, does not apply.

Measured values

quantity	value	source
replicates	1600	primary replications per scenario
n	2000	observations per primary replication
independent_tests_total	15	implementation-estimand tests against the truth
independent_tests_passed	15	of those, passing
property_cells_total	31	repeated-sampling property cells
property_cells_passed	31	cells whose own and family verdicts pass
max_standardized_bias	0.0370	largest absolute primary bias in empirical standard deviations
min_coverage	0.9425	lowest measured primary-study coverage
min_se_ratio_ci_lower	0.9400	lowest bootstrap primary SE-ratio endpoint
max_se_ratio_ci_upper	1.0892	highest bootstrap primary SE-ratio endpoint
margin:alpha	0.0500	nominal size of the reported intervals, and the family level of the rule
margin:bootstrap_replicates	10000	resamples behind every bootstrap interval
margin:calibration_coverage_lower	0.9200	calibration-cell coverage band, lower limit
margin:calibration_coverage_upper	0.9800	calibration-cell coverage band, upper limit
margin:calibration_noninferiority	0.0500	largest external-comparison calibration excess bound
margin:calibration_se_ratio_lower	0.9300	calibration-cell SE-ratio band, lower limit
margin:calibration_se_ratio_upper	1.0700	calibration-cell SE-ratio band, upper limit
margin:clustered_coverage_gain	0.0300	paired coverage-gain floor
margin:confidence_level	0.9900	confidence level of every Monte Carlo interval
margin:coverage_floor	0.9000	validity floor the coverage lower endpoint must clear
margin:coverage_noninferiority	-0.0250	smallest external-comparison coverage difference bound
margin:efficiency_ratio_lower	0.9000	efficiency-ratio lower bound
margin:efficiency_ratio_upper	1.1000	efficiency-ratio upper bound
margin:excluded_slope	-0.2500	slower rate a root-n interval must exclude
margin:iid_control_se_ceiling	0.8000	IID-control SE-ratio ceiling
margin:minimum_power	0.8000	rejection lower bound a power cell must clear
margin:nominal_coverage	0.9500	nominal coverage those intervals claim
margin:over_coverage_ceiling	0.9900	above this, coverage is conservative rather than invalid
margin:paired_difference	0.1500	paired similarity margin, in pooled empirical standard deviations
margin:rmse_noninferiority	1.1000	largest external-comparison RMSE ratio bound
margin:root_n_slope	-0.5000	contraction rate root-n asymptotics predict
margin:root_n_slope_lower	-0.6250	accepted root-n slope band, lower limit
margin:root_n_slope_upper	-0.3750	accepted root-n slope band, upper limit
margin:se_ratio_sanity_lower	0.8000	SE-ratio screen, lower limit
margin:se_ratio_sanity_upper	1.2000	SE-ratio screen, upper limit
margin:shrunken_se_factor	0.7000	negative-control SE multiplier
margin:standardized_bias	0.2500	bias equivalence margin, in empirical standard deviations
margin:targeting_displacement	0.2500	least targeting must move the estimate
margin:type_i_ceiling	0.1000	the rate a positive cell must bound and a control must exceed
margin:union_model_se_lower	0.1000	union-model SE-ratio screen, lower limit
margin:union_model_se_upper	10	union-model SE-ratio screen, upper limit

Limitations

limit	what it means for use
the comparator runs are pending	no tmle3 output is compared. The tmle3 rows of Study queue row 21 follow, and this page is Python property evidence until then
the declared tmle3 settings differ	the bounds, the censoring convention, the step and the stopping rule differ by construction. The pending pairs read under the default margins and not as an exactness check
one primary scenario	the 15 truth tests read the five-visit survival law. The other laws enter the property cells only
learners that contain the truth	the primary fit uses unpenalized logistic regressions. The property cells use correct additive or saturated learners, apart from the declared wrong ones. A flexible learner is outside the evidence
the isotonic cell does not bind	the diagnostic reports a binding share of zero, so no cell supports an interval after a projection that moves a level
the per-horizon algorithm has three cells only	targeting="iterative" appears in one calibration, one weighted and one clustered cell. It has no double-robustness or necessity cell here. The hazard study holds those cells
no cross-fitting	every fit uses one fold. The cross-fitted study covers five folds

Reproduction

The manifest records the seeds, the margins, the estimator configuration, the source hashes and the result hashes. It records the comparator as pending, with the runner and the image source hashes. Run python -m tests.canonical.point_survival_curve.regenerate to regenerate the artifacts. The replications and the property results carry every published row.