Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood

DOI10.1080/15598608.2017.1299058zbMath1425.62135OpenAlexW2594796955WikidataQ55322301 ScholiaQ55322301MaRDI QIDQ2321978

Yanyan Li

Publication date: 27 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5966290

zbMATH Keywords

penalized maximum likelihoodBayesian estimatescause-specific hazards modelfirst risk setshifted Jeffreys-type priorzero events

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Numerical analysis or methods applied to Markov chains (65C40) Estimation in survival analysis and censored data (62N02)

Related Items (3)

Firth adjusted score function for monotone likelihood in the mixture cure fraction model ⋮ Inference in the presence of likelihood monotonicity for proportional hazards regression ⋮ Modified score function for monotone likelihood in the semiparametric mixture cure model

Uses Software

• coxphf

Cites Work

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• A Solution to the Problem of Monotone Likelihood in Cox Regression
• On Bayesian Analysis of Generalized Linear Models Using Jeffreys's Prior
• Partial likelihood
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• Convergence Rates and Asymptotic Standard Errors for Markov Chain Monte Carlo Algorithms for Bayesian Probit Regression
• On the Use of Cause-Specific Failure and Conditional Failure Probabilities: Examples From Clinical Oncology Data
• Matrix theory. Basic results and techniques

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