G-Computation Risk-Ratio Inference for KK Designs with Binary Responses
Source:R/inference_incidence_KK_marginal.R
InferenceIncidKKGCompRiskRatio.RdFits the same all-subject logistic working model as
InferenceIncidKKGCompRiskDiff
for a KK incidence outcome using treatment and, optionally, all recorded
covariates, then estimates the marginal (standardized, g-computation) risk
ratio \(\hat\theta = \left(n^{-1}\sum_i \hat p(1, X_i)\right) /
\left(n^{-1}\sum_i \hat p(0, X_i)\right)\) by averaging fitted-model predicted
risks under all-treated and all-control assignments over the empirical
covariate distribution (Robins 1986). Matched pairs are treated as clusters
and reservoir subjects are treated as singletons when computing the sandwich
covariance; the delta method is applied on the log-risk-ratio scale to keep
the reported ratio and its confidence interval positive, then
back-transformed for reporting.
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$should_use_design_randomization_for_incidence()).
TRUE for every other case, including every non-incidence
response type. Public, self-contained (only reads already-set
instance state, no side effects), so InferenceSuite can
check this before attempting the sentinel instead of relying on
the stop() being silently swallowed into a pval = NA
"ok" row – the single source of truth for both this check and
compute_rand_two_sided_pval()'s own guard, so the two can
never drift apart.
Value
When debug = FALSE (default), a numeric vector of length r. When
debug = TRUE, a list with: values, errors (list of character
vectors, one per iteration), warnings (list of character vectors, one per
iteration), num_errors, num_warnings,
prop_iterations_with_errors, prop_iterations_with_warnings, and
prop_illegal_values.
A single logical.
Details
This estimator has likelihood_tier = "none". If the working model
fails to converge, has no treatment-arm variation, or the all-control
standardized risk is zero (undefined ratio), the estimate is marked
non-estimable via is_nonestimable().
References
Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period. Mathematical Modelling, 7(9-12), 1393-1512. doi:10.1016/0270-0255(86)90088-6
Super class
Inference -> InferenceIncidKKGCompRiskRatio
Methods
+ inherited public methods from Inference
Inference$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$supports()
InferenceIncidKKGCompRiskRatio$approximate_randomization_distribution_beta_hat_T()
Usage
InferenceIncidKKGCompRiskRatio$approximate_randomization_distribution_beta_hat_T(
r = 501,
delta = 0,
transform_responses = "none",
show_progress = TRUE,
permutations = NULL,
debug = FALSE,
zero_one_logit_clamp = .Machine$double.eps
)Arguments
rNumber of randomization vectors. Default 501.
deltaThe null difference. Default 0.
transform_responsesType of transformation. Default "none".
show_progressShow progress bar. Default TRUE.
permutationsPre-computed permutations. Default NULL.
debugIf
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. DefaultFALSE.zero_one_logit_clampThe clamping amount for exact 0 and 1 values when logging
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$add_all_subject_responses(rbinom(10, 1, 0.5))
inf = InferenceIncidKKGCompRiskRatio$new(seq_des)
inf$compute_estimate()
#> [1] 174167073
# }