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Activity Number:
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177
- Statistical Modeling of Lifetime Data: LiDS Section Student Award Session
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #311124
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Title:
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Non-Parametric Estimation of Spearman’s Rank Correlation with Bivariate Survival Data
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Author(s):
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Svetlana Eden* and Chun Li and Bryan E Shepherd
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Companies:
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Vanderbilt University and University of Southern California, Department of Preventive Medicine and Vanderbilt University
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Keywords:
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Bivariate survival;
Non-parametric;
Spearman’s correlation;
HIV;
Viral failure
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Abstract:
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We study rank-based approaches to estimate the correlation between two right-censored variables. With end-of-study censoring, it is often impossible to non-parametrically identify the complete bivariate survival distribution, and therefore it is impossible to non-parametrically compute Spearman’s rank correlation. As a solution, we propose two measures that can be non-parametrically identified. The first can be thought of as computing Spearman’s correlation after assigning the highest rank value to observations censored at the maximum follow-up times. The second is Spearman’s correlation in a restricted region where the conditional bivariate distribution can be computed. We describe population parameters for these measures and illustrate how they are similar to and different from Spearman’s correlation. We propose consistent estimators of these measures and study their use 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 antiviral therapy.
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Authors who are presenting talks have a * after their name.
