Conditional Logistic IVWC Inference (KK Designs, Binary Response)

Inverse-variance-weighted combination (IVWC) of two independently fit conditional-likelihood pieces for KK matched-pair-plus-reservoir binary designs: matched pairs are analyzed with exact conditional logistic regression (conditional_logit_fit_matched_pairs(), which conditions out the pair-specific nuisance intercept and estimates only the treatment log-odds-ratio \(\beta_T\) from discordant pairs, or the joint clogit-style likelihood when covariates are present), and reservoir subjects are analyzed with ordinary logistic regression (conditional_logit_fit_reservoir()). If \(\hat\beta_m, \hat\sigma^2_m\) and \(\hat\beta_r, \hat\sigma^2_r\) are the matched-pair and reservoir estimates and their variances, the combined estimate is the variance-weighted average $$\hat\beta_T = w^\star \hat\beta_m + (1-w^\star) \hat\beta_r, \quad w^\star = \frac{\hat\sigma^2_r}{\hat\sigma^2_r + \hat\sigma^2_m},$$ with combined variance \(\hat\sigma^2_m \hat\sigma^2_r / (\hat\sigma^2_m + \hat\sigma^2_r)\). This is the classical fixed-effects inverse-variance meta-analysis pooling formula (see Cochrane Handbook / DerSimonian-Laird), applied here to combine the two conditionally-independent likelihood contributions of a KK design rather than to pool separate studies. When only one of the two components is estimable the combined estimate falls back to that component alone. Contrast this with InferenceIncidKKCondLogitOneLik, which instead fits a single joint likelihood over both pieces (see that class's documentation) – likelihood_tier = "partial" here reflects that the matched-pair piece is a genuine conditional (partial) likelihood, but the two-piece combination itself is a closed-form Wald/meta-analytic step, not a further likelihood evaluation.

Details

Legacy class. Not fully tested in comprehensive_tests.R.

References

Fleiss, J.L., Levin, B., Paik, M.C. (2003). Statistical Methods for Rates and Proportions, 3rd ed. Wiley. (conditional logistic regression for matched pairs)

See also

InferenceIncidKKCondLogitOneLik for the one-likelihood alternative combining strategy.

Super class

Inference -> InferenceIncidKKCondLogitIVWC

Methods

Public methods

• InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_function()

• InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_cpp()

• InferenceIncidKKCondLogitIVWC$approximate_randomization_distribution_beta_hat_T()

• InferenceIncidKKCondLogitIVWC$supports_rand_pval_for_incidence()

• InferenceIncidKKCondLogitIVWC$clone()

+ inherited public methods from Inference

• Inference$capabilities()
• Inference$compute_asymp_confidence_interval()
• Inference$compute_asymp_two_sided_pval()
• Inference$compute_estimate()
• 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()

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InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_function()

Usage

InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_function(
  custom_randomization_statistic_function
)

---

InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_cpp()

Usage

InferenceIncidKKCondLogitIVWC$set_custom_randomization_statistic_cpp(fn)

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InferenceIncidKKCondLogitIVWC$approximate_randomization_distribution_beta_hat_T()

Usage

InferenceIncidKKCondLogitIVWC$approximate_randomization_distribution_beta_hat_T(
  r = 501,
  delta = 0,
  transform_responses = "none",
  show_progress = TRUE,
  permutations = NULL,
  debug = FALSE,
  zero_one_logit_clamp = .Machine$double.eps
)

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InferenceIncidKKCondLogitIVWC$supports_rand_pval_for_incidence()

Usage

InferenceIncidKKCondLogitIVWC$supports_rand_pval_for_incidence()

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InferenceIncidKKCondLogitIVWC$clone()

The objects of this class are cloneable with this method.

Usage

InferenceIncidKKCondLogitIVWC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.