Prospective individual patient data meta-analysis: evaluating convalescent plasma for COVID-19

DOI10.1002/SIM.9115zbMATH Open1546.62265MaRDI QIDQ6628017

Keith S. Goldfeld, Thaddeus Tarpey, Yinxiang Wu, Andrea B. Troxel, Danni Wu, Eva Petkova, Mengling Liu

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

stopping rulesBayesian data and safety monitoringinternational consortium for data sharing from ongoing RCTsstatistical analysis plan

Mathematics Subject Classification ID

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

Cites Work

• Performance guarantees for individualized treatment rules
• A class of k-sample tests for comparing the cumulative incidence of a competing risk
• Checking the Cox model with cumulative sums of martingale-based residuals
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning
• Optimal Dynamic Treatment Regimes
• A constrained single‐index regression for estimating interactions between a treatment and covariates
• Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown

This page was built for publication: Prospective individual patient data meta-analysis: evaluating convalescent plasma for COVID-19

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6628017)