Joint modeling of binary response and survival for clustered data in clinical trials

DOI10.1002/SIM.8403zbMATH Open1546.62145MaRDI QIDQ6627304

Jia Wang, Bingshu E. Chen

Publication date: 29 October 2024

Published in: (Search for Journal in Brave)

zbMATH Keywords

survival analysisLaplace transformationjackkniferandom effects modelpenalized likelihoodgeneralized linear mixed modelmarker response

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Title not available (Why is that?)
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• A penalized likelihood approach to joint modeling of longitudinal measurements and time-to-event data
• A review on joint models in biometrical research
• Analysis of longitudinal and survival data: joint modeling, inference methods, and issues
• Nonparametric inference for a family of counting processes
• Fully Exponential Laplace Approximations for the Joint Modelling of Survival and Longitudinal Data
• Seamlessly Expanding a Randomized Phase II Trial to Phase III
• Partial likelihood
• Marginal Mean Models for Dynamic Regimes
• Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood
• Approximate Inference in Generalized Linear Mixed Models
• Joint modelling of longitudinal binary data and survival data
• A Version of the EM Algorithm for Proportional Hazard Model with Random Effects
• Statistical models based on counting processes

Related Items (3)

Multilevel joint frailty model for hierarchically clustered binary and survival data ⋮ Design of phase III trials with long-term survival outcomes based on short-term binary results ⋮ Multi-parameter regression survival modelling with random effects

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