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- W438909319 abstract "Estimation of correlation when both variables are potentially right censored is confounded by identifiability issues and typically requires assumptions about the nature of the association. Semiparametric approaches have included the use of copulas, frailties or mixed models. Given that correlation is most meaningful within the context of linear association we consider iterative processes via alternating or 'criss-cross' censored linear regressions to estimate the bivariate correlation, under the assumption that each variable regresses linearly on the other. Two approaches are considered. In the first, value fragments corresponding to re-distributed censored responses from one regression are imputed as weighted explanatory variables in the alternative and the process iterated. Correlation is estimated via the two sets of slope parameters. The second replaces the weighted values by data augmentation. The efficacies of the approaches are compared and illustrated via simulations. Our initial results suggest that both methods compensate well for the censoring even with a significant proportion of cases having both variables censored, with the data augmentation approach slightly less biased. Additional (uncensored) covariates can be readily incorporated. We demonstrate the method via analysis of correlated immunological and virological measures on HIV-1 positive patients from the WAH IV Cohort, which may be incomplete for a variety of reasons." @default.
- W438909319 created "2016-06-24" @default.
- W438909319 creator A5086087903 @default.
- W438909319 date "2010-01-01" @default.
- W438909319 modified "2023-09-27" @default.
- W438909319 title "Correlation estimation with bivariate censored data via alternating regressions" @default.
- W438909319 hasPublicationYear "2010" @default.
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