A Simple Local Sensitivity Analysis Tool for Nonignorable Coarsening: Application to Dependent Censoring

DOI10.1111/j.1541-0420.2006.00580.xzbMath1114.62103OpenAlexW1964119288WikidataQ79420229 ScholiaQ79420229MaRDI QIDQ5295390

Daniel F. Heitjan, Jiameng Zhang

Publication date: 27 July 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2006.00580.x

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01)

Related Items (12)

Bayesian sensitivity analysis to the non-ignorable missing cause of failure for hybrid censored competing risks data ⋮ Statistical inference for competing risks model under progressive interval censored Weibull data ⋮ Analysis of local sensitivity to nonignorability with missing outcomes and predictors ⋮ Survival analysis with censored data: a further twist on ignorability conditions ⋮ A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout ⋮ Semiparametric estimation method for accelerated failure time model with dependent censoring ⋮ Regression Survival Analysis with an Assumed Copula for Dependent Censoring: A Sensitivity Analysis Approach ⋮ An Index of Local Sensitivity to Nonignorability for a Pseudolikelihood Method ⋮ Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models ⋮ Comments on: Missing data methods in longitudinal studies: a review ⋮ A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data ⋮ Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data

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• Estimates of marginal survival for dependent competing risks based on an assumed copula
• Informative Drop-Out in Longitudinal Data Analysis
• Sensitivity analysis for informative censoring in parametric survival models

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