Regression analysis of clustered interval-censored failure time data with the additive hazards model

DOI10.1080/10485252.2012.720256zbMath1253.62026OpenAlexW2052295115WikidataQ30939962 ScholiaQ30939962MaRDI QIDQ3145415

Junlong Li, Jianguo Sun, Chunjie Wang

Publication date: 20 December 2012

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

Full work available at URL: http://europepmc.org/articles/pmc4407380

zbMATH Keywords

interval censoringclustered dataestimating equationsemi-parametric regression analysis

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Reliability and life testing (62N05)

Related Items (5)

A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model ⋮ Robust regression analysis for clustered interval-censored failure time data ⋮ Transformation models with informative partly interval-censored data ⋮ Estimation of partly linear additive hazards model with left-truncated and right-censored data ⋮ Regression analysis of informative current status data with the additive hazards model

Cites Work

• A transformation approach for the analysis of interval-censored failure time data
• Regression estimation using multivariate failure time data and a common baseline hazard function model
• Efficient estimation for the proportional hazards model with interval censoring
• The statistical analysis of interval-censored failure time data.
• Semiparametric regression analysis for clustered failure time data
• On assessing the association for bivariate current status data
• Additive Mixed Effect Model for Clustered Failure Time Data
• Additive hazards regression with current status data
• Efficient estimation in additive hazards regression with current status data
• Efficiency Considerations in the Additive Hazards Model with Current Status Data
• Modeling Clustered, Discrete, or Grouped Time Survival Data with Covariates

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