BartCure estimates causal cure and latency effects in survival data with a cured subpopulation using Bayesian causal machine learning and is applied to the CALGB 40101 breast cancer trial.
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Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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Bayesian Causal Machine Learning for Cure Models
BartCure estimates causal cure and latency effects in survival data with a cured subpopulation using Bayesian causal machine learning and is applied to the CALGB 40101 breast cancer trial.
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Partial identification of principal causal effects under violations of principal ignorability
Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.