LWA-style Marginal Cox IVWC Compound Inference for KK Designs

Fits a compound (IVWC) estimator for KK matching-on-the-fly designs with survival responses: matched pairs are analyzed with a marginal Cox model \(\lambda(t \mid w) = \lambda_0(t)\exp(\beta_T w)\) whose robust variance uses the Lee-Wei-Amato (1992) cluster-robust sandwich (treating each matched pair as an independent cluster of correlated failure times), while reservoir subjects are analyzed with a standard (independent-subjects) Cox partial likelihood; the two log-hazard-ratio estimates are then combined by inverse-variance weighting. likelihood_tier = "partial" (Cox partial likelihood), but likelihood-ratio/score/gradient tests are not exposed on this IVWC compound (only on the OneLik sibling, which fits one combined partial likelihood across both sources instead of pooling two separate fits).

Computes the randomization distribution of the treatment effect estimate under the sharp null.

Whether compute_rand_two_sided_pval() is actually usable on this instance right now – FALSE exactly when it would stop(): an incidence-response instance with no custom randomization statistic and a design not eligible for design-randomization-based incidence inference (see private$should_use_design_randomization_for_incidence()). TRUE for every other case, including every non-incidence response type. Public, self-contained (only reads already-set instance state, no side effects), so InferenceSuite can check this before attempting the sentinel instead of relying on the stop() being silently swallowed into a pval = NA "ok" row – the single source of truth for both this check and compute_rand_two_sided_pval()'s own guard, so the two can never drift apart.

Computes a randomization-based p-value.

Value

When debug = FALSE (default), a numeric vector of length r. When debug = TRUE, a list with: values, errors (list of character vectors, one per iteration), warnings (list of character vectors, one per iteration), num_errors, num_warnings, prop_iterations_with_errors, prop_iterations_with_warnings, and prop_illegal_values.

A single logical.

Randomization p-value.

Details

Legacy class. Not fully tested in comprehensive_tests.R.

References

Lee, E. W., Wei, L. J., and Amato, D. A. (1992). "Cox-Type Regression Analysis for Large Numbers of Small Groups of Correlated Failure Time Observations." In Survival Analysis: State of the Art, 237-247. Springer. doi:10.1007/978-94-015-7983-4_14

Super class

Inference -> InferenceSurvivalKKLWACoxPHIVWC

Methods

Public methods

• InferenceSurvivalKKLWACoxPHIVWC$approximate_randomization_distribution_beta_hat_T()

• InferenceSurvivalKKLWACoxPHIVWC$supports_rand_pval_for_incidence()

• InferenceSurvivalKKLWACoxPHIVWC$compute_rand_two_sided_pval()

• InferenceSurvivalKKLWACoxPHIVWC$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()

---

InferenceSurvivalKKLWACoxPHIVWC$approximate_randomization_distribution_beta_hat_T()

Usage

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

Arguments

r

Number of randomization vectors. Default 501.

r

Number of randomization vectors.

delta

The null difference. Default 0.

delta

Null difference.

transform_responses

Type of transformation. Default "none".

transform_responses

Transformation.

show_progress

Show progress bar. Default TRUE.

show_progress

Show progress.

permutations

Pre-computed permutations. Default NULL.

permutations

Pre-computed permutations.

debug

If TRUE, return a list with the distribution values and per-iteration diagnostics including error messages, warning messages, counts of each, and summary proportions for iterations with errors, warnings, and illegal (non-finite) values. Runs serially. Default FALSE.

zero_one_logit_clamp

The clamping amount for exact 0 and 1 values when logging

zero_one_logit_clamp

The clamping amount for exact 0 and 1 values when logging

---

InferenceSurvivalKKLWACoxPHIVWC$supports_rand_pval_for_incidence()

Usage

InferenceSurvivalKKLWACoxPHIVWC$supports_rand_pval_for_incidence()

---

InferenceSurvivalKKLWACoxPHIVWC$compute_rand_two_sided_pval()

Usage

InferenceSurvivalKKLWACoxPHIVWC$compute_rand_two_sided_pval(
  r = 501,
  delta = 0,
  transform_responses = "none",
  na.rm = TRUE,
  show_progress = TRUE,
  permutations = NULL,
  zero_one_logit_clamp = .Machine$double.eps
)

Arguments

r

Number of randomization vectors. Default 501.

r

Number of randomization vectors.

delta

The null difference. Default 0.

delta

Null difference.

transform_responses

Type of transformation. Default "none".

transform_responses

Transformation.

na.rm

Remove NAs.

show_progress

Show progress bar. Default TRUE.

show_progress

Show progress.

permutations

Pre-computed permutations. Default NULL.

permutations

Pre-computed permutations.

zero_one_logit_clamp

The clamping amount for exact 0 and 1 values when logging

zero_one_logit_clamp

The clamping amount for exact 0 and 1 values when logging

---

InferenceSurvivalKKLWACoxPHIVWC$clone()

The objects of this class are cloneable with this method.

Usage

InferenceSurvivalKKLWACoxPHIVWC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{
seq_des = DesignSeqOneByOneKK14$new(n = 10, response_type = 'survival')
for (i in 1:10) {
  seq_des$add_one_subject_to_experiment_and_assign(data.frame(x1 = rnorm(1), x2 = rnorm(1)))
}
seq_des$add_all_subject_responses(runif(10))
inf = InferenceSurvivalKKLWACoxPHIVWC$new(seq_des)
inf$compute_estimate()
#> [1] 0.1683213
# }