Flexible Weighted Log-Rank Tests Optimal for Detecting Early and/or Late Survival Differences

DOI10.1111/j.0006-341X.2002.00997.xzbMath1210.62216WikidataQ44556132 ScholiaQ44556132MaRDI QIDQ3079059

Peter B. Gilbert, Lang Wu

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

asymptoticsAIDSrobust testversatile testquadratic weight

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Testing in survival analysis and censored data (62N03)

Related Items (11)

Hypothesis testing for an extended cox model with time-varying coefficients ⋮ A Nonparametric Test Procedure Based on Quantiles for the Right Censored Data ⋮ Semiparametric regression analysis of two-sample current status data, with applications to tumorigenicity experiments ⋮ A weighted log-rank test for comparing two survival curves ⋮ A permutation test for the two-sample right-censored model ⋮ A Nonparametric Test Procedure Based on a Group of Quantile Tests ⋮ Maximum of the weighted Kaplan–Meier tests for the two-sample censored data ⋮ On the versatility of the combination of the weighted log-rank statistics ⋮ Using integrated weighted survival difference for the two-sample censored data problem ⋮ On the Fleming-Harrington test for late effects in prevention randomized controlled trials ⋮ Pair chart test for an early survival difference

Uses Software

• QSIMVN

Cites Work

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• Maximum of the Weighted Kaplan-Meier Tests with Application to Cancer Prevention and Screening Trials
• Simultaneous Inferences on the Contrast of Two Hazard Functions with Censored Observations
• A Linear Rank Test for Use When the Main Interest Is in Differences in Cure Rates
• Weighted log rank type statistics for comparing survival curves when there is a time lag in the effectiveness of treatment
• Supremum Versions of the Log-Rank and Generalized Wilcoxon Statistics
• A Class of Weighted Log-Rank Tests for Survival Data When the Event is Rare
• On Robust Procedures
• Comparison of the Variance of Minimum Variance and Weighted Least Squares Regression Coefficients

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