Semiparametric maximum likelihood estimation for the Cox model with length-biased survival data

DOI10.1016/J.JSPI.2017.11.004zbMath1392.62290OpenAlexW2777153428MaRDI QIDQ1642747

Xu An Wang, Xiao-Hua Andrew Zhou

Publication date: 15 June 2018

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2017.11.004

zbMATH Keywords

length-biased survival datasemiparametric maximum likelihood estimatortemplate prevalent cohort study

Mathematics Subject Classification ID

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

Cites Work

• Estimation for semiparametric transformation models with length-biased sampling
• Weak convergence and empirical processes. With applications to statistics
• Asymptotic behavior of the unconditional NPMLE of the length-biased survivor function from right censored prevalent cohort data
• A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling
• Pseudo-partial likelihood for proportional hazards models with biased-sampling data
• Efficient estimation of semiparametric transformation models for counting processes
• Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model
• Multiplicative censoring, renewal processes, deconvolution and decreasing density: Nonparametric estimation
• Nonparametric Estimation from Cross-Sectional Survival Data
• Length-Biased Sampling With Right Censoring
• Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia
• Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models
• Statistical Models for Prevalent Cohort Data
• A formal test for the stationarity of the incidence rate using data from a prevalent cohort study with follow-up

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