Regression Analysis for Multistate Models Based on a Pseudo‐value Approach, with Applications to Bone Marrow Transplantation Studies

DOI10.1111/j.1467-9469.2006.00526.xzbMath1142.62053OpenAlexW2042384388MaRDI QIDQ5430592

Per Kragh Andersen, John P. Klein

Publication date: 16 December 2007

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

Full work available at URL: https://doi.org/10.1111/j.1467-9469.2006.00526.x

zbMATH Keywords

Markov modeljackknifetransition probabilitygeneralized estimating equationgeneralized linear regression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Generalized linear models (logistic models) (62J12) Markov processes: estimation; hidden Markov models (62M05) Bootstrap, jackknife and other resampling methods (62F40)

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Cites Work

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• Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models
• Regression analysis of restricted mean survival time based on pseudo-observations
• Testing equality of cause-specific hazard rates corresponding to \(m\) competing risks among \(K\) groups
• Inference for Events With Dependent Risks in Multiple Endpoint Studies
• Regression modeling of competing crude failure probabilities
• Generalised linear models for correlated pseudo-observations, with applications to multi-state models
• Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Prediction of Cumulative Incidence Function under the Proportional Hazards Model
• Some Simple Robust Methods for the Analysis of Recurrent Events
• Multi-state models for event history analysis
• Competing risks as a multi-state model
• Additive hazards Markov regression models illustrated with bone marrow transplant data
• Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
• Statistical models based on counting processes

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