A Solution to the Problem of Monotone Likelihood in Cox Regression

DOI10.1111/j.0006-341X.2001.00114.xzbMath1209.62024OpenAlexW2112600232WikidataQ52929496 ScholiaQ52929496MaRDI QIDQ3078689

Georg Heinze, Michael Schemper

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2001.00114.x

zbMATH Keywords

bias reductionsurvival analysisproportional hazards modelseparationprofile likelihoodpenalized likelihoodinfinite estimatesmodified score

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Censored data models (62N01) Estimation in survival analysis and censored data (62N02) Testing in survival analysis and censored data (62N03)

Related Items

Firth's penalized method in Cox proportional hazard framework for developing predictive models for sparse or heavily censored survival data ⋮ Predictive performance of penalized beta regression model for continuous bounded outcomes ⋮ Firth adjusted score function for monotone likelihood in the mixture cure fraction model ⋮ Firth adjustment for Weibull current-status survival analysis ⋮ Penalized maximum likelihood estimation in the modified extended Weibull distribution ⋮ Frailty modelling approaches for semi-competing risks data ⋮ Inference in the presence of likelihood monotonicity for proportional hazards regression ⋮ Modified score function for monotone likelihood in the semiparametric mixture cure model ⋮ Multivariate survival analysis in big data: A divide‐and‐combine approach ⋮ Bayesian solution to the monotone likelihood in the standard mixture cure model ⋮ Dealing with monotone likelihood in a model for speckled data ⋮ Bias‐reduced estimators of conditional odds ratios in matched case‐control studies with unmatched confounding ⋮ A generic algorithm for reducing bias in parametric estimation ⋮ Robust weighted kernel logistic regression in imbalanced and rare events data ⋮ Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood

Uses Software

• NAG
• nag
• SAS
• SAS/STAT

Cites Work

• On the bootstrap and monotone likelihood in the Cox proportional hazards regression model
• On bias reduction in exponential and non-exponential family regression models
• Bias Reduction using Stochastic Approximation
• Bias reduction of maximum likelihood estimates
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