Nonparametric estimation of Spearman's rank correlation with bivariate survival data

2021/03/11 by Svetlana K. Eden, Svetlana Eden, Chun Li +1 · 88 citations
Mathematics · #Advanced Statistical Methods and Models #Bivariate analysis #Bivariate data #Correlation #Econometrics #Economics #Estimation #Mathematics #Nonparametric statistics #Rank (graph theory) #Rank correlation #Spearman's rank correlation coefficient #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics

paper · open access · doi:10.1111/biom.13453

published in Biometrics 78(2), 421-434 (Oxford University Press)

openalex publication_date 2021/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

We study rank-based approaches to estimate the correlation between two right-censored variables. With end-of-study censoring, it is often impossible to nonparametrically identify the complete bivariate survival distribution, and therefore it is impossible to nonparametrically compute Spearman's rank correlation. As a solution, we propose two measures that can be nonparametrically estimated. The first measure is Spearman's correlation in a restricted region. The second measure is Spearman's correlation for an altered but estimable joint distribution. We describe population parameters for these measures and illustrate how they are similar to and different from the overall Spearman's correlation. We propose consistent estimators of these measures and study their performance through simulations. We illustrate our methods with a study assessing the correlation between the time to viral failure and the time to regimen change among persons living with HIV in Latin America who start antiretroviral therapy.

Citations

• A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence

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