Inference for A Sequential Design

An abstract R6 Class that provides asymptotic tests and intervals for a treatment effect in a sequential design where the common denominator is a summary table from a glm.

Super classes

Inference -> InferenceRand -> InferenceRandCI -> InferenceNonParamBootstrap -> InferenceRandBootstrap -> InferenceRandBootstrapCI -> InferenceBayesianBootstrap -> InferenceJackknife -> InferenceAsymp -> InferenceMLEorKMSummaryTable

Methods

Public methods

• InferenceMLEorKMSummaryTable$compute_estimate()

• InferenceMLEorKMSummaryTable$compute_asymp_confidence_interval()

• InferenceMLEorKMSummaryTable$compute_asymp_two_sided_pval()

• InferenceMLEorKMSummaryTable$clone()

+ inherited public methods from InferenceAsymp

• InferenceAsymp$compute_wald_confidence_interval()
• InferenceAsymp$compute_wald_two_sided_pval()
• InferenceAsymp$get_mod()
• InferenceAsymp$get_summary()
• InferenceAsymp$get_supported_testing_types()
• InferenceAsymp$set_testing_type()

+ inherited public methods from InferenceJackknife

• InferenceJackknife$approximate_jackknife_distribution_beta_hat_T()
• InferenceJackknife$compute_jackknife_bias_estimate()
• InferenceJackknife$compute_jackknife_estimate()
• InferenceJackknife$compute_jackknife_std_error()
• InferenceJackknife$compute_jackknife_wald_confidence_interval()
• InferenceJackknife$compute_jackknife_wald_two_sided_pval()

+ inherited public methods from InferenceBayesianBootstrap

• InferenceBayesianBootstrap$approximate_bayesian_bootstrap_distribution_beta_hat_T()
• InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval()
• InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval()
• InferenceBayesianBootstrap$compute_estimate_with_bootstrap_weights()
• InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_ci_types()
• InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_pval_types()

+ inherited public methods from InferenceRandBootstrapCI

• InferenceRandBootstrapCI$compute_rand_bootstrap_confidence_interval()
• InferenceRandBootstrapCI$get_supported_rand_bootstrap_ci_types()

+ inherited public methods from InferenceRandBootstrap

• InferenceRandBootstrap$approximate_rand_bootstrap_distribution_beta_hat_T()
• InferenceRandBootstrap$compute_rand_bootstrap_two_sided_pval()
• InferenceRandBootstrap$get_supported_rand_bootstrap_pval_types()

+ inherited public methods from InferenceNonParamBootstrap

• InferenceNonParamBootstrap$approximate_bootstrap_distribution_beta_hat_T()
• InferenceNonParamBootstrap$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()
• InferenceNonParamBootstrap$approximate_subsampling_distribution_beta_hat_T()
• InferenceNonParamBootstrap$compute_bootstrap_confidence_interval()
• InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval()
• InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_confidence_interval()
• InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_two_sided_pval()
• InferenceNonParamBootstrap$compute_subsampling_confidence_interval()
• InferenceNonParamBootstrap$compute_subsampling_sensitivity()
• InferenceNonParamBootstrap$compute_subsampling_two_sided_pval()
• InferenceNonParamBootstrap$get_supported_bootstrap_ci_types()
• InferenceNonParamBootstrap$get_supported_bootstrap_pval_types()
• InferenceNonParamBootstrap$select_optimal_b_subsampling()
• InferenceNonParamBootstrap$select_optimal_m_out_of_n_bootstrap()

+ inherited public methods from InferenceRandCI

• InferenceRandCI$compute_rand_confidence_interval()
• InferenceRandCI$compute_rand_two_sided_pval()

+ inherited public methods from InferenceRand

• InferenceRand$approximate_randomization_distribution_beta_hat_T()
• InferenceRand$set_custom_randomization_statistic_cpp()
• InferenceRand$set_custom_randomization_statistic_function()
• InferenceRand$supports_rand_pval_for_incidence()

+ 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$initialize()
• Inference$is_nonestimable()
• Inference$set_optimization_alg()
• Inference$set_seed()
• Inference$supports()

---

InferenceMLEorKMSummaryTable$compute_estimate()

Computes the appropriate estimate for mean difference

Usage

InferenceMLEorKMSummaryTable$compute_estimate(estimate_only = FALSE)

Arguments

estimate_only

If TRUE, skip variance component calculations.

Returns

The setting-appropriate (see description) numeric estimate of the treatment effect

Examples

seq_des = DesignSeqOneByOneBernoulli$new(n = 6, response_type = "continuous")
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[1, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[2, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[3, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[4, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[5, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[6, 2 : 10])
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))

seq_des_inf = InferenceContinMultOLS$new(seq_des)
seq_des_inf$compute_estimate()

---

InferenceMLEorKMSummaryTable$compute_asymp_confidence_interval()

Computes a 1-alpha level frequentist confidence interval

Usage

InferenceMLEorKMSummaryTable$compute_asymp_confidence_interval(alpha = 0.05)

Arguments

alpha

The confidence level in the computed confidence interval is 1 - alpha. The default is 0.05.

Returns

A (1 - alpha)-sized frequentist confidence interval for the treatment effect

---

InferenceMLEorKMSummaryTable$compute_asymp_two_sided_pval()

Compute a two-sided p-value for model-summary-table inference by using the cached treatment estimate and standard error from the fitted model or Kaplan-Meier summary. See InferenceMLEorKMSummaryTable and InferenceAsymp.

Usage

InferenceMLEorKMSummaryTable$compute_asymp_two_sided_pval(delta = 0)

Arguments

delta

The null difference to test against. For any treatment effect at all this is set to zero (the default).

Returns

The approximate frequentist p-value

---

InferenceMLEorKMSummaryTable$clone()

The objects of this class are cloneable with this method.

Usage

InferenceMLEorKMSummaryTable$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `InferenceMLEorKMSummaryTable$compute_estimate()`
## ------------------------------------------------

if (FALSE) { # \dontrun{
seq_des = DesignSeqOneByOneBernoulli$new(n = 6, response_type = "continuous")
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[1, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[2, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[3, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[4, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[5, 2 : 10])
seq_des$add_one_subject_to_experiment_and_assign(MASS::biopsy[6, 2 : 10])
seq_des$add_all_subject_responses(c(4.71, 1.23, 4.78, 6.11, 5.95, 8.43))

seq_des_inf = InferenceContinMultOLS$new(seq_des)
seq_des_inf$compute_estimate()
} # }