Survival Analysis Using Auxiliary Variables Via Multiple Imputation, with Application to AIDS Clinical Trial Data

DOI10.1111/j.0006-341X.2002.00037.xzbMath1209.62283WikidataQ32066278 ScholiaQ32066278MaRDI QIDQ3078902

Cheryl L. Faucett, Nathaniel Schenker

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

longitudinal datarandom effectsAIDSmissing datadependent censoringchange-point model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Medical applications (general) (92C50) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (10)

Augmented likelihood for incorporating auxiliary information into left-truncated data ⋮ A Shrinkage Approach for Estimating a Treatment Effect Using Intermediate Biomarker Data in Clinical Trials ⋮ Multiple imputation methods for recurrent event data with missing event category ⋮ Partial marginal likelihood estimation for general transformation models ⋮ Estimating the prevalence of atrial fibrillation from a three-class mixture model for repeated diagnoses ⋮ Using categorical markers as auxiliary variables in log-rank tests and hazard ratio estimation ⋮ Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia ⋮ Modified estimators for the change point in hazard function ⋮ Analysis of longitudinal and survival data: joint modeling, inference methods, and issues ⋮ Landmark estimation of survival and treatment effects in observational studies

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