BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.
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A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
Optimizing the functional form of allocation probability tests yields near-maximal power gains in response-adaptive trials for binary, normal, and exponential survival endpoints while preserving strict type I error control.
citing papers explorer
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BAMIFun: Bayesian Multiple Imputation for Functional Data
BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.
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Separable Effects in Four-Arm and Two-Arm Designs
A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
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Operationalizing Allocation Probability Tests: Practical Guidance on Optimized Implementation for Power and Robustness
Optimizing the functional form of allocation probability tests yields near-maximal power gains in response-adaptive trials for binary, normal, and exponential survival endpoints while preserving strict type I error control.
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