Bayesian sensitivity analysis to the non-ignorable missing cause of failure for hybrid censored competing risks data
From MaRDI portal
Publication:5036867
DOI10.1080/00949655.2020.1773464OpenAlexW3032942664MaRDI QIDQ5036867
Elham Mosayebi Omshi, F. Azizi, Samaneh Eftekhari Mahabadi
Publication date: 23 February 2022
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2020.1773464
sensitivity analysiscompeting risksBayesian approachtype-I hybrid censoringmissing cause of failurenon-ignorability
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout
- Analysis of incomplete data in presence of competing risks.
- Exact likelihood inference based on type-I and type-II hybrid censored samples from the exponential distribution
- A simple algorithm for generating random variates with a log-concave density
- Bayes estimation for the Marshall-Olkin bivariate Weibull distribution
- Bayesian analysis of progressively censored competing risks data
- Impact of nonignorable coarsening on Bayesian inference
- Analysis of hybrid censored competing risks data
- Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
- Nonparametric Estimation from Incomplete Observations
- Competing Exponential Risks, with Particular Reference to the Study of Smoking and Lung Cancer
- Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure
- Inference and missing data
- Expressing the Kaplan-Meier Estimator as a Function of Empirical Subsurvival Functions
- Bayesian Analysis for the Poly-Weibull Distribution
- Exact inference for competing risks model with generalized type-I hybrid censored exponential data
- A Simple Local Sensitivity Analysis Tool for Nonignorable Coarsening: Application to Dependent Censoring
This page was built for publication: Bayesian sensitivity analysis to the non-ignorable missing cause of failure for hybrid censored competing risks data