Log-Rank Inference for Survival Data with Censoring
Source:R/inference_survival_log_rank.R
InferenceSurvivalLogRank.RdNon-parametric all-subject inference for survival outcomes supporting right
censoring, based on the standard two-sample log-rank test. The treatment effect
estimate is the difference in mean martingale residuals between the treatment and
control groups under the pooled null hazard. The p-value uses the classic
log-rank score statistic with its hypergeometric tie-adjusted variance. For
left- or interval-censored data, this class dispatches instead to
interval::ictest(..., scores = "logrank1") (Sun's-scores interval-censored
generalization of the log-rank test) for both the point estimate and testing.
References
Mantel, N. (1966). "Evaluation of survival data and two new
rank order statistics arising in its consideration." Cancer
Chemotherapy Reports, 50(3), 163-170, for the log-rank test. Peto, R.,
and Peto, J. (1972). "Asymptotically Efficient Rank Invariant Test
Procedures." Journal of the Royal Statistical Society, Series A,
135(2), 185-207, doi:10.2307/2344317
, for its asymptotic-efficiency
properties and the \(\rho=0\) case of the Fleming-Harrington family
this class corresponds to (see also
InferenceSurvivalGehanWilcox
for the \(\rho=1\) member of the same family). Sun, J. (1996). "A
non-parametric test for interval-censored failure time data with
application to AIDS studies." Statistics in Medicine, 15(13),
1387-1395, for the interval-censored generalization used on the
left-/interval-censored path.
Super class
Inference -> InferenceSurvivalLogRank
Methods
Public methods
+ inherited public methods from Inference
Inference$capabilities()Inference$compute_exact_confidence_interval()Inference$compute_exact_two_sided_pval_for_treatment_effect()Inference$duplicate()Inference$get_analysis_data()Inference$get_covariates()Inference$get_design_object()Inference$get_model_formula()Inference$get_nonestimable_reason()Inference$get_nonestimable_stage()Inference$get_optimization_alg()Inference$get_response()Inference$get_response_type()Inference$get_treatment()Inference$is_nonestimable()Inference$set_optimization_alg()Inference$set_seed()Inference$supports()
InferenceSurvivalLogRank$new()
Uses the shared randomization two-sided p-value contract; see
InferenceRand.
Initialize log-rank survival inference and prepare the
treatment-group survival data used by
InferenceSurvivalLogRank.
Usage
InferenceSurvivalLogRank$new(des_obj, model_formula = NULL, verbose = FALSE)Arguments
des_objThe design object.
model_formulaOptional formula for covariate adjustment. If
NULL(default), the formula from the design object is used and its pre-computed design matrix is reused. If a formula is provided, a new design matrix is constructed from the design's imputed covariates.verboseIf TRUE, print additional information.
InferenceSurvivalLogRank$compute_estimate()
Computes the treatment-effect estimate on the martingale-residual mean-difference scale.
Under left-/interval-censored data, dispatched instead through
interval::ictest()'s Sun's-scores log-rank test (TODO-7,
interval_censored_survival_response.md); its estimate field
(mean score difference between groups) is on the same "difference of
group-mean scores" scale as the right-censored martingale-residual
difference this method otherwise returns.
InferenceSurvivalLogRank$compute_estimate_with_bootstrap_weights()
Recomputes the class-specific treatment estimate under bootstrap weights; see
InferenceBayesianBootstrap.
InferenceSurvivalLogRank$compute_asymp_confidence_interval()
Computes a (1 - alpha)-level confidence interval based on the asymptotic normality of the martingale-residual mean-difference estimate. Falls back to bootstrap if the estimated standard error is unavailable.
InferenceSurvivalLogRank$compute_asymp_two_sided_pval()
Computes a Wald-style 2-sided p-value by inverting the confidence interval.
InferenceSurvivalLogRank$compute_asymp_log_rank_two_sided_pval_for_treatment_effect()
Computes the standard two-sided log-rank p-value for a zero treatment effect.
Under left-/interval-censored data, this is interval::ictest()'s
own p-value (TODO-7) rather than a re-derived chi-squared statistic.
InferenceSurvivalLogRank$compute_rand_confidence_interval()
Randomization confidence intervals are not supported for this class because the martingale-residual score scale is not commensurate with the transformed time-ratio null used by the randomization CI algorithm.
Examples
set.seed(1)
x_dat <- data.frame(
x1 = c(-1.2, -0.7, -0.2, 0.3, 0.8, 1.3, 1.8, 2.3),
x2 = c(0, 1, 0, 1, 0, 1, 0, 1)
)
seq_des <- DesignSeqOneByOneBernoulli$
new(
n = nrow(x_dat),
response_type = "survival",
verbose = FALSE
)
for (i in seq_len(nrow(x_dat))) {
seq_des$
add_one_subject_to_experiment_and_assign(x_dat[i, , drop = FALSE])
}
seq_des$
add_all_subject_responses(
ys = c(1.2, 2.4, NA, 3.1, NA, 4.0, 3.3, NA),
y_Ls = c(NA, NA, 1.8, NA, 2.7, NA, NA, 4.5),
y_Rs = c(NA, NA, Inf, NA, Inf, NA, NA, Inf)
)
infer <- InferenceSurvivalLogRank$
new(
seq_des,
verbose = FALSE
)
infer
#> <InferenceSurvivalLogRank>
#> Inherits from: <Inference>
#> Public:
#> approximate_bayesian_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_jackknife_distribution_beta_hat_T: function (unit = "auto")
#> approximate_m_out_of_n_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_rand_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_randomization_distribution_beta_hat_T: function (r = 501, delta = 0, transform_responses = "none", show_progress = TRUE,
#> approximate_subsampling_distribution_beta_hat_T: function (...)
#> capabilities: function ()
#> clone: function (deep = FALSE)
#> compute_asymp_confidence_interval: function (alpha = 0.05)
#> compute_asymp_log_rank_two_sided_pval_for_treatment_effect: function (delta = 0)
#> compute_asymp_two_sided_pval: function (delta = 0)
#> compute_bayesian_bootstrap_confidence_interval: function (...)
#> compute_bayesian_bootstrap_two_sided_pval: function (...)
#> compute_bootstrap_confidence_interval: function (...)
#> compute_bootstrap_two_sided_pval: function (...)
#> compute_estimate: function (estimate_only = FALSE)
#> compute_estimate_with_bootstrap_weights: function (subject_or_block_weights, estimate_only = FALSE)
#> compute_exact_confidence_interval: function (...)
#> compute_exact_two_sided_pval_for_treatment_effect: function (...)
#> compute_jackknife_bias_estimate: function (unit = "auto")
#> compute_jackknife_estimate: function (unit = "auto")
#> compute_jackknife_std_error: function (unit = "auto")
#> compute_jackknife_wald_confidence_interval: function (alpha = 0.05, unit = "auto")
#> compute_jackknife_wald_two_sided_pval: function (delta = 0, unit = "auto")
#> compute_m_out_of_n_bootstrap_confidence_interval: function (...)
#> compute_m_out_of_n_bootstrap_two_sided_pval: function (...)
#> compute_rand_bootstrap_confidence_interval: function (...)
#> compute_rand_bootstrap_two_sided_pval: function (...)
#> compute_rand_confidence_interval: function (alpha = 0.05, r = 501, pval_epsilon = 0.005, show_progress = TRUE,
#> compute_rand_two_sided_pval: function (r = 501, delta = 0, transform_responses = "none", na.rm = TRUE,
#> compute_subsampling_confidence_interval: function (...)
#> compute_subsampling_sensitivity: function (...)
#> compute_subsampling_two_sided_pval: function (...)
#> compute_wald_confidence_interval: function (alpha = 0.05)
#> compute_wald_two_sided_pval: function (delta = 0)
#> duplicate: function (verbose = FALSE, make_fork_cluster = FALSE)
#> get_analysis_data: function ()
#> get_covariates: function ()
#> get_design_object: function ()
#> get_mod: function ()
#> get_model_formula: function ()
#> get_nonestimable_reason: function ()
#> get_nonestimable_stage: function ()
#> get_optimization_alg: function ()
#> get_response: function ()
#> get_response_type: function ()
#> get_summary: function ()
#> get_supported_bayesian_bootstrap_ci_types: function (...)
#> get_supported_bayesian_bootstrap_pval_types: function (...)
#> get_supported_bootstrap_ci_types: function (...)
#> get_supported_bootstrap_pval_types: function (...)
#> get_supported_rand_bootstrap_ci_types: function (...)
#> get_supported_rand_bootstrap_pval_types: function (...)
#> get_supported_testing_types: function ()
#> get_treatment: function ()
#> initialize: function (des_obj, model_formula = NULL, verbose = FALSE)
#> is_nonestimable: function (type = c("any", "estimate", "se"))
#> num_cores: active binding
#> select_optimal_b_subsampling: function (...)
#> select_optimal_m_out_of_n_bootstrap: function (...)
#> set_custom_randomization_statistic_cpp: function (fn)
#> set_custom_randomization_statistic_function: function (custom_randomization_statistic_function)
#> set_optimization_alg: function (optimization_alg = NULL, allow_irls = private$optimization_alg_allow_irls,
#> set_seed: function (seed)
#> set_testing_type: function (testing_type = "wald")
#> supports: function (capability)
#> supports_rand_pval_for_incidence: function ()
#> Private:
#> X: -1.2 -0.7 -0.2 0.3 0.8 1.3 1.8 2.3 0 1 0 1 0 1 0 1
#> active_resampling_operation: NULL
#> allocate_resampling_sizes_by_stratum: function (...)
#> analyze_custom_randomization_statistic: function ()
#> any_censoring: TRUE
#> approximate_bayesian_bootstrap_statistics_beta_hat_T: function (...)
#> approximate_bayesian_jackknife_distribution_beta_hat_T: function (...)
#> approximate_bootstrap_statistics_beta_hat_T: function (...)
#> approximate_jackknife_distribution_beta_hat_T_private: function (...)
#> approximate_m_out_of_n_bootstrap_distribution_beta_hat_T_impl: function (...)
#> approximate_subsampling_distribution_beta_hat_T_impl: function (...)
#> assert_design_supports_randomization_draw: function (method_family)
#> assert_design_supports_resampling: function (method_family)
#> assert_design_supports_resampling_replay: function (method_family)
#> assert_exact_inference_params: function (type, args_for_type)
#> assert_jackknife_supported: function (unit = "auto")
#> assert_no_incidence_only_randomization_args: function (resp_type, type, args_for_type)
#> assert_valid_bootstrap_type: function (...)
#> bayesian_bootstrap_cache_key: function (...)
#> bayesian_bootstrap_ci_types: NULL
#> bayesian_bootstrap_pval_types: NULL
#> bayesian_bootstrap_sample_weights: function (...)
#> bca_ci_core: function (...)
#> bca_pval_core: function (...)
#> begin_rand_worker_reuse_session: function ()
#> boot_distr_cache: NULL
#> bootstrap_ci_types: NULL
#> bootstrap_confidence_interval_extreme: function (...)
#> bootstrap_estimates_extreme: function (...)
#> bootstrap_extreme_ci_width_threshold: NULL
#> bootstrap_extreme_estimate_threshold: NULL
#> bootstrap_pval_types: NULL
#> bootstrap_replication_stats: function (...)
#> bootstrap_sample_indices: function (...)
#> bootstrap_subset_inference: function (...)
#> brt_mc_control: NULL
#> build_bayesian_bootstrap_context: function (...)
#> build_fast_randomization_worker_cache: function (prev_cache = NULL, preserve_cache_keys = character())
#> build_jackknife_deletion_draws: function (...)
#> build_randomization_ci_search_bounds: function (inf_obj, r, alpha, transform_arg, permutations, ci_search_control,
#> build_randomization_distribution_cache_key: function (r, delta, transform_responses, permutations)
#> build_resampling_draw_from_units: function (...)
#> cache_nonestimable_estimate: function (reason = "not_estimable")
#> cache_nonestimable_se: function (reason = "standard_error_unavailable")
#> cached_X_full_for_reduced: NULL
#> cached_design_matrix: NULL
#> cached_harden_for_design_matrix: NULL
#> cached_hardened_X_cov: NULL
#> cached_j_treat_for_reduced: NULL
#> cached_keep_for_reduced: NULL
#> cached_reduced_X: NULL
#> cached_values: list
#> cached_vc_params: NULL
#> cached_w_for_design_matrix: NULL
#> check_bootstrap_replicate_deadline: function (...)
#> check_rand_bootstrap_ci_deadline: function (...)
#> check_randomization_ci_deadline: function (ci_search_control = NULL, label = "Randomization CI bisection")
#> ci_bayesian_bca: function (...)
#> ci_bca: function (...)
#> ci_calibrated_bootstrap: function (...)
#> ci_from_boot_distribution: function (...)
#> ci_smoothed_bootstrap: function (...)
#> ci_studentized: function (...)
#> ci_symmetric_studentized: function (...)
#> clear_fit_warm_start: function ()
#> clear_likelihood_null_warm_cache: function ()
#> clear_likelihood_test_eval_cache: function ()
#> clear_nonestimable_state: function ()
#> closed_form_ci_from_affine_null_draws: function (...)
#> compute_bayesian_bootstrap_distribution_with_reused_workers: function (...)
#> compute_bayesian_bootstrap_worker_estimate: function (...)
#> compute_bootstrap_distribution_with_reused_workers: function (...)
#> compute_bootstrap_worker_estimate: function (worker_state)
#> compute_bootstrap_worker_estimate_via_compute_treatment_estimate: function (...)
#> compute_brt_null_statistics_with_reused_workers: function (...)
#> compute_brt_null_statistics_with_se: function (...)
#> compute_ci_by_inverting_the_randomization_test_iteratively: function (r, l, u, pval_th, tol, transform_responses, lower,
#> compute_exact_confidence_interval_rand: function (type, alpha, args_for_type)
#> compute_exact_two_sided_pval_rand: function (type, delta, args_for_type)
#> compute_fast_rand_bootstrap_distr: function (y0_full, rand_bootstrap_draws, delta, transform_responses,
#> compute_fast_randomization_distr_via_reused_worker: function (y, permutations, delta, transform_responses, preserve_cache_keys = character(),
#> compute_jackknife_distribution_with_reused_workers: function (...)
#> compute_jackknife_summary: function (unit = "auto")
#> compute_m_out_of_n_bootstrap_confidence_interval_impl: function (...)
#> compute_m_out_of_n_bootstrap_two_sided_pval_impl: function (...)
#> compute_rand_bootstrap_ci_pval_cached: function (...)
#> compute_rand_bootstrap_distribution_with_reused_workers: function (...)
#> compute_randomization_ci_pval_cached: function (inf_obj, r, delta, transform_responses, permutations,
#> compute_randomization_distr_via_reused_worker_states: function (permutations, delta, transform_responses, actual_rand_cores,
#> compute_randomization_worker_estimate: function (worker_state)
#> compute_resampling_draw_distribution: function (...)
#> compute_reusable_bootstrap_worker_distribution: function (...)
#> compute_shared: function (estimate_only = FALSE)
#> compute_shared_icen: function (estimate_only = FALSE)
#> compute_subsampling_confidence_interval_impl: function (...)
#> compute_subsampling_sensitivity_impl: function (...)
#> compute_subsampling_two_sided_pval_impl: function (...)
#> compute_subsampling_worker_estimate: function (...)
#> compute_treatment_estimate_during_randomization_inference: function (estimate_only = TRUE)
#> compute_two_sided_brt_pval_studentized: function (...)
#> compute_two_sided_brt_pval_with_sequential_mc: function (...)
#> compute_two_sided_pval_with_sequential_mc: function (t, r, delta, transform_responses, show_progress, permutations,
#> compute_two_sided_randomization_pval_band: function (t0s, t, conf_level)
#> compute_two_sided_randomization_pval_from_t0s: function (t0s, t)
#> compute_wald_confidence_interval_impl: function (alpha)
#> compute_wald_two_sided_pval_impl: function (delta)
#> compute_z_or_t_ci_from_s_and_df: function (alpha)
#> compute_z_or_t_two_sided_pval_from_s_and_df: function (delta)
#> create_bootstrap_worker_state: function ()
#> create_design_backed_bootstrap_worker_state: function (...)
#> create_design_matrix: function ()
#> create_reusable_bootstrap_worker: function (...)
#> current_bayesian_bootstrap_context: NULL
#> current_bayesian_bootstrap_subject_or_block_weights: NULL
#> dead: 1 1 0 1 0 1 1 0
#> des_obj: DesignSeqOneByOneBernoulli, DesignSeqOneByOne, Design, R6
#> des_obj_priv_int: environment
#> effective_parallel_cores: function (operation, requested_cores = self$num_cores)
#> end_rand_worker_reuse_session: function ()
#> ensure_mirai_daemons: function (n)
#> ensure_resampling_distribution_cache: function (operation)
#> estimate_bootstrap_worker: function (...)
#> evaluate_lightweight_custom_randomization_statistic: function (lightweight_custom_context, y, w, dead, cpp_fn_override = NULL)
#> evaluate_m_out_of_n_bootstrap_size: function (...)
#> evaluate_subsampling_size: function (...)
#> expand_bound: function (inf_obj, bound, est, r, transform_arg, permutations,
#> expand_rand_bootstrap_bound: function (...)
#> expand_subject_or_block_weights_to_row_weights: function (...)
#> extract_dollar_paths: function (expr)
#> finalize: function ()
#> fit_warm_start: NULL
#> fit_warm_start_enabled: TRUE
#> fit_warm_start_fisher: NULL
#> fit_warm_start_type: NULL
#> fit_warm_start_weights: NULL
#> fit_with_hardened_qr_column_dropping: function (X_full, fit_fun, fit_ok, required_cols = 1L)
#> fixed_covariate_keep_cache: NULL
#> fork_cluster: NULL
#> generate_exchangeable_resampling_draws: function (...)
#> generate_permutations: function (r)
#> generate_rand_bootstrap_draws: function (...)
#> get_X: function ()
#> get_bootstrap_type: function (...)
#> get_brt_distribution_prefix: function (...)
#> get_cached_centered_resampling_pivot: function (...)
#> get_cached_resampling_distribution: function (operation, cache_key)
#> get_cluster_jackknife_ids: function (...)
#> get_compiled_cpp_stat: function ()
#> get_complexity_tier: function ()
#> get_degrees_of_freedom: function ()
#> get_estimand_type: function ()
#> get_exchangeable_units: function (...)
#> get_fit_warm_start: function (type = c("beta", "params"))
#> get_fit_warm_start_fisher: function (expected_dim = NULL)
#> get_fit_warm_start_for_length: function (type = c("beta", "params"), expected_length = NULL)
#> get_fit_warm_start_weights: function (expected_n = NULL)
#> get_likelihood_null_warm_state: function (key)
#> get_likelihood_test_eval_cache: function ()
#> get_likelihood_test_eval_entry: function (testing_type, delta)
#> get_optimal_warm_start_config: function (expected_length, expected_fisher_dim = expected_length)
#> get_or_create_fork_cluster: function ()
#> get_randomization_ci_seed_candidates: function (inf_obj, alpha)
#> get_randomization_distribution_prefix: function (r, delta, transform_responses, show_progress, permutations,
#> get_resampling_block_ids: function (...)
#> get_resampling_cluster_ids: function (...)
#> get_resampling_draw_contract: function (operation)
#> get_resampling_strata_ids: function (...)
#> get_standard_error: function ()
#> get_supported_testing_types_impl: function ()
#> get_w_signed: function (w)
#> harden: TRUE
#> has_general_censoring: FALSE
#> has_match_structure: FALSE
#> has_private_method: function (method_name)
#> high_precision_confirm_and_refine_ci_bound: function (l, u, lower, r, transform_responses, permutations,
#> infer_original_se: function (...)
#> invert_ci_to_find_two_sided_pval_for_treatment_effect: function (delta = 0)
#> invert_rand_bootstrap_test_bisection: function (...)
#> is_KK: FALSE
#> is_a_asymp: function ()
#> is_a_rand_ci: function ()
#> is_bernoulli_design: function ()
#> is_resampling_control_condition: function (...)
#> jack_distr_cache: NULL
#> jackknife_always_nonestimable: function ()
#> jackknife_block_size_gt_one_unsupported: function (unit = "auto")
#> jackknife_cache_key: function (unit = "auto")
#> likelihood_null_warm_cache: NULL
#> likelihood_test_delta_key: function (testing_type, delta)
#> lin_xm_m_vec: NULL
#> lin_xm_structural: NULL
#> load_bayesian_bootstrap_draw_into_worker: function (...)
#> load_bayesian_bootstrap_weights_into_worker: function (...)
#> load_bootstrap_draw_into_worker: function (...)
#> load_bootstrap_sample_into_design_backed_worker: function (...)
#> load_bootstrap_sample_into_worker: function (worker_state, indices)
#> load_m_out_of_n_bootstrap_draw_into_worker: function (...)
#> load_non_param_bootstrap_draw_into_worker: function (...)
#> load_rand_bootstrap_assignment_into_worker: function (...)
#> load_rand_bootstrap_draw_into_worker: function (...)
#> load_randomization_draw_into_worker: function (worker_state, draw, delta, transform_responses, setup,
#> load_randomization_perm_into_worker: function (worker_state, perm_w, delta, transform_responses, y_delta,
#> load_resampling_draw_into_worker: function (operation, worker_state, draw, ...)
#> load_subsampling_draw_into_worker: function (...)
#> m: NULL
#> m_out_of_n_bootstrap_cache_key: function (...)
#> m_out_of_n_bootstrap_centered_pivot: function (...)
#> m_out_of_n_bootstrap_sample_indices: function (...)
#> mark_jackknife_nonestimable_if_block_unsupported: function (unit = "auto")
#> missing_bootstrap_ci: function (...)
#> model_formula: formula
#> n: 8
#> n_cpp_threads: function (n_work_items)
#> normalize_delta_for_cache: function (delta, resolution = NULL)
#> normalize_exact_inference_args: function (type, args_for_type = NULL, pval_epsilon = NULL)
#> normalize_jackknife_unit: function (unit = "auto")
#> normalize_likelihood_test_delta: function (delta)
#> normalize_randomization_ci_search_control: function (ci_search_control, r, pval_epsilon)
#> null_fit_warm_start_enabled: TRUE
#> num_cores_override: NULL
#> object_has_private_method: function (obj, method_name)
#> optimization_alg: NULL
#> optimization_alg_allow_irls: FALSE
#> optimization_alg_default: lbfgs
#> p: NULL
#> par_lapply: function (X, FUN, n_cores = self$num_cores, budget = 1L, show_progress = FALSE,
#> parallel_dispatch_policy: function (operation)
#> prob_T: 0.5
#> pval_bayesian_bca: function (...)
#> pval_bca: function (...)
#> rand_boot_draws_counter: NULL
#> rand_bootstrap_ci_conservative_count: NULL
#> rand_bootstrap_ci_timeout_deadline: function (...)
#> rand_bootstrap_ci_types: NULL
#> rand_bootstrap_draw_matrices: function (...)
#> rand_bootstrap_pval_types: NULL
#> rand_bootstrap_transform_code: function (...)
#> reduce_design_matrix_preserving_treatment: function (X_full)
#> reduce_design_matrix_preserving_treatment_fixed_covariates: function (X_full)
#> reduce_design_matrix_preserving_treatment_matrix: function (X_full)
#> reduce_treatment_only_design_fast: function (X_full)
#> reduced_design_keep_cache: NULL
#> renumber_match_ids: function (...)
#> requires_blocking_design: function ()
#> resampling_centered_pval: function (...)
#> resampling_ci_from_centered_distribution: function (...)
#> resampling_effective_p: function (...)
#> resampling_error_to_na: function (...)
#> resampling_scaling_factor: function (...)
#> resampling_scaling_key: function (...)
#> resolve_dollar_path: function (expr)
#> resolve_jackknife_unit: function (unit = "auto")
#> resolve_resampling_size: function (...)
#> resolve_resampling_unit: function (...)
#> reusable_bootstrap_worker_enabled: TRUE
#> run_rand_bootstrap_iteration: function (...)
#> run_rand_bootstrap_iteration_with_se: function (...)
#> run_randomization_iteration: function (thread_des_obj, thread_inf_obj, perm_idx, permutations,
#> sample_exchangeable_unit_ids: function (...)
#> seed: NULL
#> select_optimal_b_subsampling_impl: function (...)
#> select_optimal_m_out_of_n_bootstrap_impl: function (...)
#> select_optimal_resample_size: function (...)
#> sequential_mc_band_excludes_threshold: function (t0s, t, threshold, conf_level)
#> sequential_mc_control_enabled: function (mc_ctrl)
#> set_cached_centered_resampling_pivot: function (...)
#> set_cached_resampling_distribution: function (operation, cache_key, value)
#> set_fit_warm_start: function (start, type = c("beta", "params"), fisher = NULL, weights = NULL,
#> set_likelihood_null_warm_state: function (key, delta, start)
#> set_likelihood_test_eval_entry: function (testing_type, delta, entry)
#> setup_randomization_template_and_shifts: function (delta, transform_responses, zero_one_logit_clamp = .Machine$double.eps)
#> shift_randomization_responses: function (y, w, delta, transform_responses, response_type, inverse = FALSE,
#> should_use_design_randomization_for_incidence: function ()
#> should_use_zhang_incidence_randomization: function ()
#> smart_cold_start_default: TRUE
#> stable_signature: function (obj)
#> studentized_bootstrap_pivots: function (...)
#> studentized_interval_scale_unstable: function (...)
#> subsampling_cache_key: function (...)
#> subsampling_centered_pivot: function (...)
#> subsampling_sample_indices: function (...)
#> subset_permutations: function (permutations, indices)
#> supports_bayesian_bootstrap: function (...)
#> supports_design_randomization_draw: TRUE
#> supports_design_resampling: TRUE
#> supports_design_resampling_replay: TRUE
#> supports_interval_or_left_censored_data: function ()
#> supports_reusable_bootstrap_worker: function ()
#> sync_randomization_worker_state: function (thread_des_obj, thread_inf_obj)
#> try_cached_reduced_design_keep: function (X_full, keep = private$reduced_design_keep_cache)
#> use_reusable_bootstrap_worker: function ()
#> validate_bootstrap_worker_state: function (...)
#> verbose: FALSE
#> w: 0 0 1 1 0 1 1 1
#> warned_no_parallel: FALSE
#> weighted_logrank_mean_difference: function (row_weights)
#> xm_m_vec: NULL
#> xm_structural: NULL
#> y: 1.2 2.4 1.8 3.1 2.7 4 3.3 4.5
#> y_L: NA NA 1.8 NA 2.7 NA NA 4.5
#> y_R: NA NA Inf NA Inf NA NA Inf
#> y_temp: 1.2 2.4 1.8 3.1 2.7 4 3.3 4.5