Survival model construction guided by fit and predictive strength

DOI10.1111/biom.12611zbMath1372.62060arXiv1405.6900OpenAlexW2962698111WikidataQ39320000 ScholiaQ39320000MaRDI QIDQ5283304

Cécile Chauvel, John O'Quigley

Publication date: 21 July 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.6900

zbMATH Keywords

goodness-of-fittime-varying coefficientnon-proportional hazardssurvival modelpredictive measures\(R^2\) measures

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Reliability and life testing (62N05)

Cites Work

• Unnamed Item
• Unnamed Item
• Cox's regression model for counting processes: A large sample study
• Time-dependent coefficients in a Cox-type regression model
• Survival analysis. Techniques for censored and truncated data.
• Dynamic regression models for survival data.
• Khmaladze-type graphical evaluation of the proportional hazards assumption
• Martingale-based residuals for survival models
• Tests for comparing estimated survival functions
• Testing Goodness of Fit for Proportional Hazards Model with Censored Observations
• Residuals for relative risk regression
• Measures of dependence for censored survival data
• Comparing two failure time distributions in the presence of dependent censoring
• Checking the Cox model with cumulative sums of martingale-based residuals
• Predictive capability of proportional hazards regression.
• Proportional hazards tests and diagnostics based on weighted residuals
• Local Linear Estimation for Time‐Dependent Coefficients in Cox's Regression Models
• Maximum Likelihood Estimation for Cox's Regression Model Under Case-Cohort Sampling
• A. R.²type measure of dependence for proportional hazards models

This page was built for publication: Survival model construction guided by fit and predictive strength