Conditional Logistic Plus GLMM Combined-Likelihood 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 included in the GLMM component (private$combine_reservoir_into_glmm() == TRUE), so all subjects (discordant matched pairs, concordant matched pairs, and reservoir) enter one joint likelihood with 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 InferenceIncidKKCondLogitGLMMIVWC, which excludes reservoir subjects from the GLMM component.

Super classes

Inference -> InferenceAbstractKKCondLogitGLMM -> InferenceIncidKKCondLogitGLMMOneLik

Methods

Public methods

• InferenceIncidKKCondLogitGLMMOneLik$new()

• InferenceIncidKKCondLogitGLMMOneLik$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$get_testing_type()
• InferenceAbstractKKCondLogitGLMM$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()

---

InferenceIncidKKCondLogitGLMMOneLik$new()

Initialize inference for the combined conditional-logit (discordant matched pairs) plus random-intercept-GLMM (concordant pairs and reservoir subjects) incidence model; see InferenceIncidKKCondLogitGLMMOneLik for the model form. Does not fit the model; the fit is deferred to the first call to compute_estimate() or a method that requires it.

Usage

InferenceIncidKKCondLogitGLMMOneLik$new(
  des_obj,
  model_formula = NULL,
  max_abs_reasonable_coef = 50,
  max_abs_reasonable_se = 1.25,
  max_abs_log_sigma = 8,
  verbose = FALSE,
  smart_cold_start_default = NULL,
  optimization_alg = NULL
)

Arguments

des_obj

A completed Design object with an incidence response.

model_formula

Optional formula for covariate adjustment.

max_abs_reasonable_coef

Cap for reasonable coefficient estimates.

max_abs_reasonable_se

Cap for reasonable treatment standard errors.

max_abs_log_sigma

Cap for reasonable log random effect variance.

verbose

Whether to print progress messages.

smart_cold_start_default

Whether to use smart optimizer start values.

optimization_alg

Character. Optimization algorithm (default "lbfgs").

---

InferenceIncidKKCondLogitGLMMOneLik$clone()

The objects of this class are cloneable with this method.

Usage

InferenceIncidKKCondLogitGLMMOneLik$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{
seq_des = DesignSeqOneByOneKK14$new(n = 10, response_type = 'incidence')
for (i in 1:10) {
  seq_des$add_one_subject_to_experiment_and_assign(data.frame(x1 = rnorm(1), x2 = rnorm(1)))
}
seq_des$add_all_subject_responses(rbinom(10, 1, 0.5))
inf = InferenceIncidKKCondLogitGLMMOneLik$new(seq_des)
inf$compute_estimate()
#> [1] NA
# }