Conditional Logistic Plus GLMM IVWC Inference for KK Designs

Fits a combined conditional-logit-plus-random-intercept-GLMM likelihood for incidence responses under a KK matching-on-the-fly design, where reservoir (unmatched) subjects are excluded from the GLMM component (private$combine_reservoir_into_glmm() == FALSE): only concordant matched pairs contribute their random-intercept GLMM likelihood alongside the discordant-pair conditional-logit term, both sharing a single treatment coefficient \(\beta_T\). See InferencePropKKGLMM for the full model form (conditional-logit-on-discordant plus random-intercept-GLMM, jointly maximized) and InferenceAbstractKKCondLogitGLMM for the shared fitting/caching contract. Contrast with the sibling InferenceIncidKKCondLogitGLMMOneLik, which instead includes reservoir subjects in the GLMM component (combine_reservoir_into_glmm() == TRUE) — this class's naming ("IVWC") reflects that reservoir information, when used, is intended to be combined with this fit's estimate via inverse-variance weighting rather than folded into the same likelihood.

Details

Legacy class. Not fully tested in comprehensive_tests.R.

Super classes

Inference -> InferenceAbstractKKCondLogitGLMM -> InferenceIncidKKCondLogitGLMMIVWC

Methods

Public methods

• InferenceIncidKKCondLogitGLMMIVWC$clone()

+ inherited public methods from InferenceAbstractKKCondLogitGLMM

• InferenceAbstractKKCondLogitGLMM$approximate_bayesian_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_jackknife_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_rand_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_randomization_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$approximate_subsampling_distribution_beta_hat_T()
• InferenceAbstractKKCondLogitGLMM$compute_asymp_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_asymp_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_bayesian_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_bayesian_bootstrap_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_bootstrap_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_estimate()
• InferenceAbstractKKCondLogitGLMM$compute_estimate_with_bootstrap_weights()
• InferenceAbstractKKCondLogitGLMM$compute_gradient_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_gradient_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_jackknife_bias_estimate()
• InferenceAbstractKKCondLogitGLMM$compute_jackknife_estimate()
• InferenceAbstractKKCondLogitGLMM$compute_jackknife_std_error()
• InferenceAbstractKKCondLogitGLMM$compute_jackknife_wald_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_jackknife_wald_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_approx_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_approx_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_exact_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_exact_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bootstrap_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_m_out_of_n_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_m_out_of_n_bootstrap_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_estimate()
• InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_pval()
• InferenceAbstractKKCondLogitGLMM$compute_rand_bootstrap_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_rand_bootstrap_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_rand_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_rand_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_score_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_score_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_subsampling_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_subsampling_sensitivity()
• InferenceAbstractKKCondLogitGLMM$compute_subsampling_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$compute_wald_confidence_interval()
• InferenceAbstractKKCondLogitGLMM$compute_wald_two_sided_pval()
• InferenceAbstractKKCondLogitGLMM$get_information_preference()
• InferenceAbstractKKCondLogitGLMM$get_information_source_used()
• InferenceAbstractKKCondLogitGLMM$get_last_param_bootstrap_diagnostics()
• InferenceAbstractKKCondLogitGLMM$get_last_param_bootstrap_estimate_diagnostics()
• InferenceAbstractKKCondLogitGLMM$get_mod()
• InferenceAbstractKKCondLogitGLMM$get_summary()
• InferenceAbstractKKCondLogitGLMM$get_supported_bayesian_bootstrap_ci_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_bayesian_bootstrap_pval_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_bootstrap_ci_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_bootstrap_pval_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_information_preferences()
• InferenceAbstractKKCondLogitGLMM$get_supported_rand_bootstrap_ci_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_rand_bootstrap_pval_types()
• InferenceAbstractKKCondLogitGLMM$get_supported_testing_types()
• InferenceAbstractKKCondLogitGLMM$get_testing_type()
• InferenceAbstractKKCondLogitGLMM$initialize()
• InferenceAbstractKKCondLogitGLMM$select_optimal_b_subsampling()
• InferenceAbstractKKCondLogitGLMM$select_optimal_m_out_of_n_bootstrap()
• InferenceAbstractKKCondLogitGLMM$set_custom_randomization_statistic_cpp()
• InferenceAbstractKKCondLogitGLMM$set_custom_randomization_statistic_function()
• InferenceAbstractKKCondLogitGLMM$set_information_preference()
• InferenceAbstractKKCondLogitGLMM$set_testing_type()
• InferenceAbstractKKCondLogitGLMM$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$is_nonestimable()
• Inference$set_optimization_alg()
• Inference$set_seed()
• Inference$supports()

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InferenceIncidKKCondLogitGLMMIVWC$clone()

The objects of this class are cloneable with this method.

Usage

InferenceIncidKKCondLogitGLMMIVWC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples