Robust methods to improve efficiency and reduce bias in estimating survival curves in randomized clinical trials

DOI10.1007/s10985-014-9291-yzbMath1322.62257OpenAlexW1964511345WikidataQ44648755 ScholiaQ44648755MaRDI QIDQ746638

Min Zhang

Publication date: 16 October 2015

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-014-9291-y

zbMATH Keywords

Nelson-Aalen estimatorcumulative hazard functiontime-to-eventcovariate-adjustmentKaplan-Meier curve

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02) Reliability and life testing (62N05)

Related Items (5)

Comment: Inference after covariate-adaptive randomisation: aspects of methodology and theory ⋮ Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes ⋮ Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment ⋮ Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards ⋮ Landmark estimation of survival and treatment effects in observational studies

Cites Work

• Introduction to empirical processes and semiparametric inference
• Nonparametric Estimation from Incomplete Observations
• Semiparametric Estimation of Treatment Effect in a Pretest‐Posttest Study
• Improving Efficiency of Inferences in Randomized Clinical Trials Using Auxiliary Covariates
• Asymptotic Statistics
• NONPARAMETRIC ANALYSIS OF COVARIANCE FOR HYPOTHESIS TESTING WITH LOGRANK AND WILCOXON SCORES AND SURVIVAL-RATE ESTIMATION IN A RANDOMIZED CLINICAL TRIAL
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