Non-strange Weird Resampling for Complex Survival Data

TL;DR

This paper proposes a new data-dependent multiplier bootstrap method for survival data analysis, unifying existing bootstrap techniques and providing rigorous asymptotic validation, with promising simulation and real data results.

Contribution

Introduces a novel data-dependent multiplier bootstrap for survival analysis, including wild and weird bootstrap as special cases, with proven asymptotic correctness.

Findings

Both bootstrap methods perform well in simulations.

The new method provides valid pointwise and simultaneous inference.

Application to cardiovascular data demonstrates practical utility.

Abstract

This paper introduces the new data-dependent multiplier bootstrap for non-parametric analysis of survival data, possibly subject to competing risks. The new resampling procedure includes both the general wild bootstrap and the weird bootstrap as special cases. The data may be subject to independent right-censoring and left-truncation. We rigorously prove asymptotic correctness which has in particular been pending for the weird bootstrap. As a consequence, pointwise as well as time-simultaneous inference procedures for, amongst others, the classical survival setting are deduced. We report simulation results and a real data analysis of the cumulative cardiovascular event probability. The simulation results suggest that both the weird bootstrap and use of non-standard multipliers in the wild bootstrap may perform preferably.