Bootstrapping Aalen-Johansen processes for competing risks: handicaps, solutions, and limitations

DOI10.1214/14-EJS972zbMath1309.62079arXiv1401.7801MaRDI QIDQ485928

Dennis Dobler, Markus Pauly

Publication date: 14 January 2015

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1401.7801

zbMATH Keywords

bootstrapweighted bootstrapleft-truncationcounting processesAalen-Johansen estimatorcumulative incidence functionright-censoringcompeting risk

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Nonparametric statistical resampling methods (62G09) Testing in survival analysis and censored data (62N03) Markov processes: hypothesis testing (62M02)

Related Items

A wild bootstrap approach for nonparametric repeated measurements ⋮ Wild bootstrap logrank tests with broader power functions for testing superiority ⋮ Dynamic inference in general nested case‐control designs ⋮ CASANOVA: Permutation inference in factorial survival designs ⋮ The wild bootstrap for multivariate Nelson-Aalen estimators

Uses Software

• timereg

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Studentized permutation tests for non-i.i.d. hypotheses and the generalized Behrens-Fisher problem
• Weighted Logrank Permutation Tests for Randomly Right Censored Life Science Data
• Bootstrapping and permuting paired \(t\)-test type statistics
• Resampling schemes with low resampling intensity and their applications in testing hypotheses
• A quantile based test for comparing cumulative incidence functions of competing risks models
• Two-sample tests of the equality of two cumulative incidence functions
• A class of k-sample tests for comparing the cumulative incidence of a competing risk
• When does bootstrap work! Asymptotic results and simulations
• A rank statistics approach to the consistency of a general bootstrap
• Bootstrap methods: another look at the jackknife
• Exchangeably weighted bootstraps of the general empirical process
• Resampling: consistency of substitution estimators
• How do bootstrap and permutation tests work?
• Jackknife, bootstrap and other resampling methods in regression analysis
• Nonparametric symmetry tests for statistical functionals.
• Weak convergence and empirical processes. With applications to statistics
• Weighted resampling of martingale difference arrays with applications
• Likelihood ratio tests for and against ordering of the cumulative incidence functions in multiple competing risks and discrete mark variable models
• Dynamic regression models for survival data.
• Resampling Student's \(t\)-type statistics
• A Note on Variance Estimation of the Aalen-Johansen Estimator of the Cumulative Incidence Function in Competing Risks, with a View towards Left-Truncated Data
• Weak Convergence of the Wild Bootstrap for the Aalen-Johansen Estimator of the Cumulative Incidence Function of a Competing Risk
• Competing Risks and Multistate Models with R
• Empirical Processes with Applications to Statistics
• Extensions and Applications of the Cox‐Aalen Survival Model
• Comparison of failure probabilities in the presence of competing risks
• Calibrating parametric subject-specific risk estimation
• Inference for Events With Dependent Risks in Multiple Endpoint Studies
• Checking the Cox model with cumulative sums of martingale-based residuals
• Some Tests for Comparing Cumulative Incidence Functions and Cause-Specific Hazard Rates
• Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
• Statistical models based on counting processes

This page was built for publication: Bootstrapping Aalen-Johansen processes for competing risks: handicaps, solutions, and limitations