Perturbation-Based Efficient Resampling Method for Variance Estimation in Survival Data Analysis

Music City Center 

Accurate and easy-to-implement variance estimation is essential but often challenging in complex statistical settings. Traditional approaches, such as the nonparametric bootstrap, are computationally intensive as they typically require repeatedly solving estimating equations. Moreover, bootstrapped samples almost always contain ties, introducing additional complications. These challenges are further amplified when plug-in estimators are involved, where how variability is carried from one stage to the next is less explored, and failing to account for it properly could lead to an underestimation of the variance. To address these issues, we examine resampling and perturbation methods that retain the nonparametric flexibility of the traditional bootstrap while significantly reducing computational burden by eliminating the need for repeated equation solving. Through extensive simulation, we show that the perturbation method offers an efficient and reliable alternative for variance estimation in survival data, substantially lowering computational costs compared to the bootstrap while maintaining accuracy, making it a powerful tool for statistical inference in complex models.

Perturbation

Variance Estimation

Resampling

Survival Data

Bootstrap 

Biometrics Section