Rate/Mean Regression for Multiple-Sequence Recurrent Event Data with Missing Event Category

DOI10.1111/j.1467-9469.2006.00459.xzbMath1120.62082OpenAlexW1986102952MaRDI QIDQ3411068

Jianwen Cai, Douglas E. Schaubel

Publication date: 8 December 2006

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

Full work available at URL: https://doi.org/10.1111/j.1467-9469.2006.00459.x

zbMATH Keywords

asymptotic normalitylogistic regressionproportional hazard rates model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02)

Related Items (11)

The analysis of multivariate recurrent events with partially missing event types ⋮ Multiple imputation methods for recurrent event data with missing event category ⋮ A semiparametric additive rates model for multivariate recurrent events with missing event categories ⋮ An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random ⋮ A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events ⋮ Proportional hazards regression in the presence of missing study eligibility information ⋮ Proportional Rate Model with Incomplete Covariate and Complete Auxiliary Information ⋮ Regression analysis of multivariate recurrent event data with a dependent terminal event ⋮ Semiparametric additive marginal regression models for multiple type recurrent events ⋮ Analysis of cyclic recurrent event data with multiple event types ⋮ Regression analysis of multivariate recurrent event data with time-varying covariate effects

Cites Work

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• Towards a general asymptotic theory for Cox model with staggered entry
• Marginal means/rates models for multiple type recurrent event data
• Weak convergence and empirical processes. With applications to statistics
• Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
• Asymptotic Statistics
• Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
• Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure

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