Derives new analytical sample size formulas for the marginal hazard ratio under IPW estimation in Cox models, correcting classic log-rank formulas for RCTs and adding an overlap measure for observational data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 2years
2026 2representative citing papers
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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Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size formulas for the marginal hazard ratio under IPW estimation in Cox models, correcting classic log-rank formulas for RCTs and adding an overlap measure for observational data.
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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.