A class of proportional win‐fractions regression models for composite outcomes

DOI10.1111/BIOM.13382zbMath1520.62281OpenAlexW3089211817WikidataQ99713888 ScholiaQ99713888MaRDI QIDQ6055478

Tuo Wang, Lu Mao

Publication date: 30 October 2023

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.13382

zbMATH Keywords

\(U\)-processeswin ratioproportionality assumptionprobabilistic index modelscardiovascular trialsprioritized endpoints

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Nonparametric Inference of General While-Alive Estimands for Recurrent Events ⋮ Semiparametric Estimation of Probabilistic Index Models: Efficiency and Bias ⋮ On restricted mean time in favor of treatment ⋮ Unnamed Item ⋮ Sample size formula for general win ratio analysis

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• An alternative approach to confidence interval estimation for the win ratio statistic
• Semiparametric Box–Cox power transformation models for censored survival observations
• Misspecified proportional hazard models
• Bivariate Survival Models Induced by Frailties
• Checking the Cox model with cumulative sums of martingale-based residuals
• On the alternative hypotheses for the win ratio
• On the win-ratio statistic in clinical trials with multiple types of event
• Probabilistic Index Models

This page was built for publication: A class of proportional win‐fractions regression models for composite outcomes