The Fine-Gray model under interval censored competing risks data

DOI10.1016/j.jmva.2015.10.001zbMath1328.62280OpenAlexW2195859266WikidataQ31019011 ScholiaQ31019011MaRDI QIDQ900812

Chenxi Li

Publication date: 23 December 2015

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2015.10.001

zbMATH Keywords

semiparametric efficiencyinterval censored datacumulative incidence functionsieve estimationcompeting risksubdistribution hazard

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (7)

Analysis of interval-censored competing risks data under missing causes ⋮ Cause-specific hazard regression for competing risks data under interval censoring and left truncation ⋮ Inferences on cumulative incidence function for middle censored survival data with Weibull regression ⋮ Semiparametric Regression Analysis of Interval-Censored Competing Risks Data ⋮ Sieve Estimation of a Class of Partially Linear Transformation Models With Interval-Censored Competing Risks Data ⋮ Uniform convergence rate of the nonparametric maximum likelihood estimator for current status data with competing risks ⋮ A comparative study on the prediction of occupational diseases in China with hybrid algorithm combing models

Uses Software

• cmprsk

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Two likelihood-based semiparametric estimation methods for panel count data with covariates
• Current status data with competing risks: consistency and rates of convergence of the MLE
• Current status data with competing risks: Limiting distribution of the MLE
• Convergence rate of sieve estimates
• An adaptive barrier method for convex programming
• Weak convergence and empirical processes. With applications to statistics
• Introduction to empirical processes and semiparametric inference
• A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval-Censored Data
• Nonparametric Maximum Likelihood Estimation for Competing Risks Survival Data Subject to Interval Censoring and Truncation
• The Analysis of Failure Times in the Presence of Competing Risks
• Asymptotic Statistics
• Sieve Estimation for the Proportional-Odds Failure-Time Regression Model With Interval Censoring
• Regression modeling of competing crude failure probabilities
• Nonparametric estimation from current status data with competing risks
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Consistency of the GMLE with Mixed Case Interval‐Censored Data
• Parametric likelihood inference for interval censored competing risks data
• Smoothed nonparametric estimation for current status competing risks data
• Direct Parametric Inference for the Cumulative Incidence Function

This page was built for publication: The Fine-Gray model under interval censored competing risks data