Joint modeling of event time and nonignorable missing longitudinal data

DOI10.1023/A:1014871806118zbMath1030.62081OpenAlexW1560617808WikidataQ30696388 ScholiaQ30696388MaRDI QIDQ1812096

Jean-François Dupuy, Mounir Mesbah

Publication date: 18 June 2003

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1014871806118

zbMATH Keywords

EM-algorithmnonparametric likelihoodnonignorable dropoutmissing covariate valuestime-dependent Cox model

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Related Items (9)

Estimation of the Asymptotic Variance of Semiparametric Maximum Likelihood Estimators in the Cox Model with a Missing Time-Dependent Covariate ⋮ Using the Score Test to Identify the Longitudinal Biomarker Considering Accelerate Failure Time Model with the Frailty in Survival Analysis ⋮ A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women ⋮ Analysis of longitudinal health-related quality of life data with terminal events ⋮ Comparing crossing hazard rate functions by joint modelling of survival and longitudinal data ⋮ Asymptotic theory for the Cox model with missing time-dependent covariate ⋮ Semiparametric transformation joint models for longitudinal covariates and interval-censored failure time ⋮ The proportional hazards model with covariate measurement error ⋮ Asymptotic normality of semiparametric estimators in the Cox model with nonignorable missing covariate

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